“image acquisition and manipulation protocols for ct-pet fusion … · 2010-09-13 · “image...

“Image acquisition and manipulation protocols for CT-PET

fusion to improve the accuracy of gross tumour volume

localisation for 3D conformal radiotherapy for lung cancer.”

Catriona Hargrave BAppSc: Medical Radiations Technology - Radiotherapy, QUT

Faculty of Science and Technology

Queensland University of Technology

Thesis submitted in fulfilment of the requirements for

the degree of Master of Applied Science (Research)

2010

i

Key words

Image registration, image fusion, tumour volume delineation, clinical protocols,

computed tomography, CT, positron emission tomography, PET, tumour motion, 3-

dimensional conformal radiation therapy, treatment planning, lung cancer.

ii

Abstract

Aims: To develop clinical protocols for acquiring PET images, performing CT-PET

registration and tumour volume definition based on the PET image data, for

radiotherapy for lung cancer patients and then to test these protocols with respect to

levels of accuracy and reproducibility.

Method: A phantom-based quality assurance study of the processes associated with

using registered CT and PET scans for tumour volume definition was conducted to:

(1) investigate image acquisition and manipulation techniques for registering and

contouring CT and PET images in a radiotherapy treatment planning system, and (2)

determine technology-based errors in the registration and contouring processes. The

outcomes of the phantom image based quality assurance study were used to

determine clinical protocols. Protocols were developed for (1) acquiring patient PET

image data for incorporation into the 3DCRT process, particularly for ensuring that

the patient is positioned in their treatment position; (2) CT-PET image registration

techniques and (3) GTV definition using the PET image data. The developed clinical

protocols were tested using retrospective clinical trials to assess levels of inter-user

variability which may be attributed to the use of these protocols. A Siemens

Somatom Open Sensation 20 slice CT scanner and a Philips Allegro stand-alone PET

scanner were used to acquire the images for this research. The Philips Pinnacle3

Results: Both the attenuation-corrected and transmission images obtained from

standard whole-body PET staging clinical scanning protocols were acquired and

imported into the treatment planning system for the phantom-based quality assurance

study. Protocols for manipulating the PET images in the treatment planning system,

particularly for quantifying uptake in volumes of interest and window levels for

accurate geometric visualisation were determined. The automatic registration

algorithms were found to have sub-voxel levels of accuracy, with transmission scan-

based CT-PET registration more accurate than emission scan-based registration of

the phantom images. Respiration induced image artifacts were not found to

influence registration accuracy while inadequate pre-registration over-lap of the CT

and PET images was found to result in large registration errors. A threshold value

treatment planning system was used to perform the image registration and contouring

of the CT and PET images.

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based on a percentage of the maximum uptake within a volume of interest was found

to accurately contour the different features of the phantom despite the lower spatial

resolution of the PET images. Appropriate selection of the threshold value is

dependant on target-to-background ratios and the presence of respiratory motion.

The results from the phantom-based study were used to design, implement and test

clinical CT-PET fusion protocols. The patient PET image acquisition protocols

enabled patients to be successfully identified and positioned in their radiotherapy

treatment position during the acquisition of their whole-body PET staging scan.

While automatic registration techniques were found to reduce inter-user variation

compared to manual techniques, there was no significant difference in the

registration outcomes for transmission or emission scan-based registration of the

patient images, using the protocol. Tumour volumes contoured on registered patient

CT-PET images using the tested threshold values and viewing windows determined

from the phantom study, demonstrated less inter-user variation for the primary

tumour volume contours than those contoured using only the patient’s planning CT

scans.

Conclusions: The developed clinical protocols allow a patient’s whole-body PET

staging scan to be incorporated, manipulated and quantified in the treatment planning

process to improve the accuracy of gross tumour volume localisation in 3D

conformal radiotherapy for lung cancer. Image registration protocols which factor in

potential software-based errors combined with adequate user training are

recommended to increase the accuracy and reproducibility of registration outcomes.

A semi-automated adaptive threshold contouring technique incorporating a PET

windowing protocol, accurately defines the geometric edge of a tumour volume

using PET image data from a stand alone PET scanner, including 4D target volumes.

iv

Table of contents

1 Background ......................................................................................................... 1

1.1 Introduction ................................................................................................ 1

1.2 Literature review and background information ..................................... 5

1.2.1 Guidelines for tumour volume definition in radiotherapy ................... 5

1.2.2 Over view of the 3D conformal radiotherapy process ......................... 6

1.2.3 Lung cancer patients referred for 3D conformal radiotherapy ............. 8

1.2.4 Limitations of CT in GTV localisation for lung cancers ..................... 8

1.2.5 The sensitivity and specificity of PET versus CT for imaging lung

cancer ................................................................................................... 9

1.2.6 The rationale of image fusion in treatment planning ........................... 9

1.2.7 The potential of CT/PET fusion to improve GTV definition accuracy

for lung cancers .................................................................................. 13

1.2.8 Potential errors associated with incorporating registered CT/PET

images into the GTV definition process for lung cancers .................. 13

1.2.9 Planning CT and 18 15F-FDG PET image acquisition and quality .........

1.2.9.1 Planning CT image acquisition ...................................................... 15

1.2.9.2 PET image acquisition and reconstruction ..................................... 18

1.2.9.3 PET image quantification ............................................................... 21

1.2.9.4 The effects of motion on CT and PET image quality .................... 22

1.2.9.5 The implications of clinical PET scan access for patient positioning

........................................................................................................ 24

1.2.10 Image viewing and interpretation ....................................................... 25

1.2.11 Image registration techniques ............................................................. 28

1.2.11.1 Manual registration .................................................................... 29

1.2.11.2 Semi-automatic registration techniques ..................................... 29

1.2.11.3 Automatic registration techniques .............................................. 32

1.2.11.4 Validation of image registration ................................................. 38

1.2.12 Contouring techniques ........................................................................ 38

1.2.12.1 Contouring techniques in a treatment planning system ............. 38

1.2.12.2 Reported methods of GTV contouring on PET images ............. 40

1.3 Aims of the project ................................................................................... 42

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1.4 Ethical considerations .............................................................................. 43

1.5 Research agreement ................................................................................. 46

2 Image acquisition and analysis ....................................................................... 47

2.1 Phantom design and construction .......................................................... 47

2.1.1 Aims of phantom construction ........................................................... 47

2.1.2 Phantom dimensions and functions .................................................... 48

2.1.2.1 Main tank design ............................................................................ 48

2.1.2.2 Moving sphere design .................................................................... 51

2.2 Phantom image acquisition and analysis ............................................... 53

2.2.1 Aims ................................................................................................... 53

2.2.2 Methodology ...................................................................................... 54

2.2.2.1 Phantom CT scan acquisition ......................................................... 54

2.2.2.2 Phantom PET scan acquisition ....................................................... 61

2.2.2.3 Image quantification using the treatment planning system tools ... 65

2.2.2.4 Determination of window width and level for PET images ........... 69

2.2.2.5 Evaluation of the moving sphere on the CT and PET images ....... 72

2.2.3 Data analysis ...................................................................................... 73

2.2.4 Results ................................................................................................ 76

2.2.4.1 Phantom CT scan acquisition ......................................................... 76

2.2.4.2 Phantom PET scan acquisition ....................................................... 79

2.2.4.3 Image quantification using the treatment planning system tools ... 84

2.2.4.4 Determination of window widths and levels for PET images ....... 90

2.2.4.5 Evaluation of the imaged moving sphere on the CT and PETimages

........................................................................................................ 92

2.2.5 Discussion and conclusions ............................................................... 97

2.3 Patient image acquisition and analysis ................................................. 103

2.3.1 Aims ................................................................................................. 103

2.3.2 Methodology .................................................................................... 104

2.3.2.1 Estimation of patient image numbers for protocol trials .............. 104

2.3.2.2 Clinical protocols for acquiring patient PET scans ...................... 104

2.3.2.3 PET scan acquisition .................................................................... 105

2.3.2.4 CT scan acquisition ...................................................................... 106

2.3.2.5 Image analysis .............................................................................. 108

2.3.2.6 Application of phantom-based image windowing results ............ 109

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2.3.3 Results .............................................................................................. 110

2.3.3.1 Patient identification and PET scan acquisition protocols ........... 110

2.3.3.2 Image analysis .............................................................................. 112

2.3.3.3 Application of phantom-based image windowing results ............ 112

2.3.4 Discussion and conclusions .............................................................. 116

3 Image registration technique evaluation and development of a protocol for

the clinical trials ............................................................................................. 120

3.1 Phantom based image registration technique evaluation ................... 120

3.1.1 Aims ................................................................................................. 120

3.1.2 Methodology .................................................................................... 121

3.1.2.1 Algorithm tests ............................................................................. 121

3.1.2.2 Baseline registrations ................................................................... 123

3.1.2.3 Auto-registration of the planning CT and PET AC scans using the

MI algorithm ................................................................................ 125

3.1.2.4 Automated PET transmission scan-based registration with the

planning CT scan using the MI algorithm .................................... 125

3.1.2.5 Fiducial marker tests .................................................................... 125

3.1.3 Data analysis .................................................................................... 127

3.1.3.1 Cross correlation and mutual information algorithm tests ........... 127

3.1.3.2 Comparison of auto-registration results of the phantom CT and PET

images ........................................................................................... 127


3.1.4 Results .............................................................................................. 131

3.1.4.1 Algorithm tests ............................................................................. 131

3.1.4.2 Comparison of the level of accuracy and reproducibility of AC

scan-based registration versus transmission scan-based-registration

...................................................................................................... 133

3.1.4.3 The effects of motion on the level of accuracy and reproducibility

of automated registration techniques ............................................ 137


3.1.5 Discussion and conclusions .............................................................. 145

3.2 Clinical trial of the image registration protocol .................................. 149

3.2.1 Aims ................................................................................................. 149

3.2.2 Methodology .................................................................................... 150

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3.2.2.1 Pilot study and RT training .......................................................... 150

3.2.2.2 RT image registration trials using patient data ............................ 152

3.2.3 Data analysis .................................................................................... 154

3.2.4 Results .............................................................................................. 156

3.2.4.1 Pilot study and RT training .......................................................... 156

3.2.4.2 RT image registration trials using patient data ............................ 157

3.2.5 Discussion and conclusions ............................................................. 166

4 GTV delineation technique evaluation and protocol development for the

clinical trial ..................................................................................................... 168

4.1 Phantom contouring tests ...................................................................... 168

4.1.1 Aims ................................................................................................. 168

4.1.2 Methodology .................................................................................... 169

4.1.2.1 Threshold value contouring of phantom PET AC data ................ 169

4.1.2.2 Verification of the geometrical accuracy of the threshold values 170

4.1.3 Data analysis .................................................................................... 173

4.1.4 Results .............................................................................................. 174

4.1.4.1 Threshold value contouring of phantom PET AC data ................ 174


4.2 Clinical trial of the GTV delineation protocol ..................................... 181

4.2.1 Aims ................................................................................................. 181

4.2.2 Methodology .................................................................................... 182

4.2.2.1 Pilot study and RO training .......................................................... 182

4.2.2.2 RO contouring trial using patient data ......................................... 184

4.2.3 Data analysis .................................................................................... 186

4.2.4 Results .............................................................................................. 187

4.2.4.1 Pilot study and RO training .......................................................... 187

4.2.4.2 RO contouring trial using patient data ......................................... 188


5 Conclusions and recommendations .............................................................. 199

Appendix 1: Phantom PET scan pixel and SUV graphs .................................... 200

Appendix 2: Graphs of the registration algorithm test results .......................... 204

Appendix 3: Graphs of the phantom registration results .................................. 209

Appendix 4: RT instructions for the patient image registration trials ............. 214

Appendix 5: Graphs of the results for the RT registration trials ...................... 217

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Appendix 6: RO instructions for the patient GTV definition trials .................. 220

6 Bibliography ................................................................................................... 230

ix

List of figures

Figure 1-1 The 3D conformal radiotherapy treatment process ................................... 7

Figure 1-2 2D and 3D images in a treatment planning system reconstructed from the

planning CT ........................................................................................................ 10

Figure 1-3 Dose distribution calculated using a planning CT scan for a lung cancer

patient ................................................................................................................. 11

Figure 1-4 Image fusion software visualisation tools ............................................... 12

Figure 1-5 Partial volume effects caused by thick CT slices .................................... 17

Figure 1-6 Comparison of a patient’s non-attenuation corrected and an attenuation-

corrected emission scan ..................................................................................... 20

Figure 1-7 Image characteristics of PET emission, transmission and attenuation-

corrected emission scans .................................................................................... 21

Figure 1-8 Motion artifacts on a free-breathing planning CT scan of the chest ....... 23

Figure 1-9 Lung and mediastinal CT viewing windows for the chest region ........... 26

Figure 1-10 Effect of different window levels on apparent dimensions of a tumour 28

Figure 1-11 The direction of the translation and rotations of the secondary image

about the image axes relative to the patient ....................................................... 29

Figure 1-12 Example of multiple solutions for contour-based registration .............. 30

Figure 1-13 The potential error in fiducial marker localisation as a factor of slice

thickness and marker size ................................................................................... 31

Figure 1-14 Semi-automated threshold contouring techniques ................................. 39

Figure 2-1 Main tank design: INF / Feet view .......................................................... 48

Figure 2-2 Main tank design: Side view at the mid-tank sagittal plane .................... 50

Figure 2-3 Photograph of moving sphere, including mount and motor .................... 52

Figure 2-4 Photograph of phantom set up for scanning ............................................ 54

Figure 2-5 Moving sphere scanning position in main tank: INF / Feet view ............ 55

Figure 2-6 Central sphere sup/inf scanning position in main tank: side view at the

mid-tank sagittal plane ....................................................................................... 56

Figure 2-7 MM3003 multimodality fiducial markers ............................................... 57

Figure 2-8 Position of fiducial markers on phantom ................................................ 57

Figure 2-9 Scanning positions of the sphere to simulate a 4D CT volume of the

moving lesion ..................................................................................................... 60

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Figure 2-10 Fiducial marker injection technique ...................................................... 62

Figure 2-11 Contouring of the central rods on scan 3 from the “static” CT scan series

of the phantom .................................................................................................... 66

Figure 2-12 Creation of the 4D models of the moving sphere .................................. 67

Figure 2-13 3D models positioned and converted to contours on the PET AC images

of the phantom .................................................................................................... 68

Figure 2-14 PET AC image pixel data available using the CT/dose tool ................. 69

Figure 2-15 Visualisation of the methods used for determining appropriate window

width and levels for viewing PET images .......................................................... 71

Figure 2-16 Determining appropriate viewing windows for the moving sphere on the

PET images using the 4D model as a template .................................................. 72

Figure 2-17 Images of the series of static CT scans of the phantom ......................... 76

Figure 2-18 Images of series 1 and 2 of the “free-breathing” CT scans of the

phantom .............................................................................................................. 77

Figure 2-19 Images of series 3 and 4 of the “free-breathing” CT scans of the

phantom .............................................................................................................. 78

Figure 2-20 The first test PET scan ........................................................................... 79

Figure 2-21 Test PET scans with 0.0197 MBq/ml concentration of 18-FDG .......... 80

Figure 2-22 Appearance of the fiducial markers on the PET AC images ................. 80

Figure 2-23 AC emission scan images from the different phantom PET scan series 82

Figure 2-24 Transmission scan images from the different phantom PET scan series

............................................................................................................................ 83

Figure 2-25 Plots of the background-activity ratios of the main tank pixel data to the

sphere and central rod pixel data for the PET AC images: Series 5-8 with

conditions as per Table 2-4. ............................................................................... 89

Figure 2-26 Frequency plots of window widths and levels for viewing different

features on the PET transmission scans of the phantom .................................... 90

Figure 2-27 Frequency plots of window widths and levels for viewing different

features on the PET AC scans of the phantom ................................................... 91

Figure 2-28 Overlaid 3D contours of the moving sphere imaged for each scan from

the different free breathing series of CT scans ................................................... 92

Figure 2-29 Graphical representation of the variation in the imaged superior and

inferior aspect of the moving sphere from the free breathing CT scans ............ 94

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Figure 2-30 Visual comparison of moving sphere imaged on PET AC scans with the

4D model: Series 4 and 6. .................................................................................. 95

Figure 2-31 Visual comparison of moving sphere imaged on PET AC scans with the

4D model: Series 7 and 8 ................................................................................... 96

Figure 2-32 Method for obtaining image data from the PET AC patient images ... 108

Figure 2-33 PET image viewing window results: Patients 1 - 3 ............................. 113

Figure 2-34 PET image viewing window results: Patients 4 – 5 ............................ 114

Figure 2-35 PET image viewing window results: Patients 7 – 9 ............................ 115

Figure 3-1 Box plots for the registration results for the series 2 PET images of the

phantom ............................................................................................................ 134

Figure 3-2 The standard deviation plotted against the mean for the post registration

parameters: combined AC and transmission scan-based registrations ............ 135

Figure 3-3 Combined registration results for the CT and PET images with all

components of the phantom static .................................................................... 138

Figure 3-4 Combined registration results for the CT and PET images with the sphere

moving ............................................................................................................. 139


parameters – all components of phantom static ............................................... 141

Figure 3-6 The standard deviation plotted against the mean – with the sphere moving

during scanning ................................................................................................ 141

Figure 3-7 Anatomy-based matching criteria for the baseline registrations of the

patient CT and PET data sets ........................................................................... 151

Figure 3-8 Patient positioning and misregistration issues noted during the pilot study

.......................................................................................................................... 156

Figure 3-9 Combined RT results of the patient CT and PET images by registration

technique .......................................................................................................... 158


parameters: automated and manual RT registration results ............................. 159


parameters: AC scan and transmission scan-based automated RT registration

results ............................................................................................................... 160


parameters: manual RT results based on the order the registrations were

performed ......................................................................................................... 163

xii


parameters: automated RT results based on the order the registrations were

performed ......................................................................................................... 164

Figure 4-1 Creation of the 20% threshold contour for the 0.5 cm rod using the semi-

automated contouring technique ...................................................................... 170

Figure 4-2 Verification of the geometrical accuracy of the threshold values ......... 172

Figure 4-3 Threshold values plotted against the volume of interest (central rod)

diameter ............................................................................................................ 175

Figure 4-4 A profile of the pixel values taken through the identified GTV on a PET

AC image .......................................................................................................... 184

Figure 4-5 RO contouring results: Patients 2 – 4 .................................................... 189

Figure 4-6 RO contouring results: Patients 5 – 7 .................................................... 190

Figure 4-7 RO contouring results: Patients 8 and 9 ................................................ 191

Figure 4-8 The percentage differences for the combined RO contours: Patients 2 – 4

.......................................................................................................................... 193


.......................................................................................................................... 194

Figure 4-10 The percentage differences for the combined RO contours: Patients 8

and 9 ................................................................................................................. 195

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List of tables

Table 1-1 Medicare funded criteria and codes for whole body PET for lung cancer

(November 2004)72 25 ............................................................................................

Table 1-2 Threshold methods for contouring a GTV using the PET image data ...... 40

Table 2-1 Capacity (ml) of different compartments in phantom ............................... 51

Table 2-2 The “static” phantom CT scan acquisition parameters ............................. 59

Table 2-3 Phantom conditions for the “free breathing” phantom CT scans.............. 59

Table 2-4 Different PET scanning conditions of the phantom for images to be used

to test image registration and GTV delineation protocols ................................. 64

Table 2-5 Volumes of the components of the phantom on CT – calculated and TPS

generated volumes using different contouring methods .................................... 84

Table 2-6 Volume of the 4D moving sphere on CT – calculated and TPS generated

volumes using different contouring methods ..................................................... 85

Table 2-7 Intra-series mean volumes and the overall mean volumes of the 3D model-

generated contours of the phantom components on the PET AC images .......... 86

Table 2-8 Percentage difference of the 3D model-generated volumes for both the CT

and PET images to the calculated volumes of the phantom components .......... 86

Table 2-9 Average ratios of the maximum pixel value in each rod or the sphere to

the 3.0 cm rod maximum pixel value ................................................................. 87

Table 2-10 Imaged volumes of the moving spherical lesion from the different free

breathing series of CT scans .............................................................................. 93

Table 2-11 Patients whose PET images were not used in the clinical trials ........... 110

Table 2-12 Summary of the patient clinical data whose images were acquired for the

image registration and GTV definition trials ................................................... 111

Table 2-13 Patient GTV, Liver and Lung VOI data ................................................ 112

Table 3-1 Pre-registration offsets of secondary image from the primary for algorithm

reproducibility tests .......................................................................................... 121

Table 3-2 Translation only pre-registration offsets of secondary image from the

primary for algorithm accuracy tests ................................................................ 122

Table 3-3 Translation and rotation pre-registration offsets of secondary image from

the primary for algorithm accuracy tests .......................................................... 123

Table 3-4 Registered CT and PET AC scans of the phantom ................................ 124

xiv

Table 3-5 Summary of the results of the CC and MI algorithm tests ...................... 132

Table 3-6 Repeatability coefficients for the different image-based automated

registration techniques ...................................................................................... 136

Table 3-7 Paired t-test comparisons of AC and transmission scan-based mean

registration results of the phantom CT and PET images .................................. 137

Table 3-8 Repeatability coefficients for the static phantom or moving sphere images

.......................................................................................................................... 142

Table 3-9 T-test comparisons of static or moving sphere for the AC and transmission

scan-based mean registration results of the phantom CT and PET images ..... 143

Table 3-10 The difference in fiducial marker localisation on registered CT and PET

AC images of the phantom ............................................................................... 144

Table 3-11 Repeatability coefficients for the manual and automated RT registration

results ............................................................................................................... 161

Table 3-12 Paired t-test comparisons of the means of the registration parameters for

the RT manual and automated registrations ..................................................... 161

Table 3-13 t-test comparisons of the means of the registration parameters for the RT

AC and transmission scan-based registrations ................................................. 162

Table 3-14 Repeatability coefficients based on the order that the RT performed the

registrations ...................................................................................................... 165

Table 4-1 Threshold values verified for GTV definition by visual match with CT 174

Table 4-2 Mean volumes of the baseline contours of the phantom compared to those

generated using the verified accurate threshold values .................................... 176

Table 4-3 Comparison of the percentage maximum threshold value for the SUV data

based on the contouring results ........................................................................ 177

Table 4-4 Results of the pilot study used to determine the threshold values for

contouring the GTV on the patient PET AC images ........................................ 187

Table 4-5 The mean volumes of the RO contours .................................................. 192

Table 4-6 The percentage differences in the volumes of the RO contours ............ 192

xv

List of abbreviations

3DCRT 3 dimensional conformal

radiation therapy 18F-FDG 18

Flourine-2-flouro-2-deoxy-D-glucose

68Ge 68

Germanium

137Cs 137

Cesium

AC attenuation- corrected AEC automatic exposure

control AMPR adaptive multiplanar

reconstruction BEV beam’s eye view Bq Becquerel CC cross correlation CT computed tomography CTV clinical tumour volume DICOM Digital Imaging and

Communication in Medicine

DRR digitally reconstructed

radiograph FOV field of view GTV gross tumour volume ICRU International Commission

on Radiation Units and Measurements

IM internal margin MBS model based segmentation

MI mutual information MPR multi-planar

reconstruction MRI magnetic resonance

imaging MSCT multi-slice computed

tomography NSCLC non-small cell lung cancer PET positron emission

tomography PTV planning tumour volume QLD Queensland RAMLA row-action maximisation-

likelihood algorithm RO radiation oncologist ROI region of interest RT radiation therapist SCLC small cell lung cancer SM set-up margin SUV standardised uptake value TBR target-to-background ratio TPS treatment planning system VOI volume of interest WW window width WL window level

xv

Statement of original authorship

“The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously published

or written by another person except where due reference is made.”

Signature

Date

xvi

Acknowledgements

The Mater Radiation Oncology Centre, the Mater Hospital, South Brisbane, QLD

Australia, in conjunction with the Princess Alexandra Hospital, Annerley,

Brisbane. The Mater Radiation Oncology Centre was the principle clinical

collaborator for this research project. Access was provided to the Pinnacle

Research collaboration and support

3

computer planning system for the conduct of the RO and RT trials as well as the

planning CT scanner for scanning of the phantom. The Mater also supplied funding

for the purchase of the multi-modality fiducial markers used on the phantom images.

Many thanks to the radiation oncologists and radiation therapists who participated in

the image registration and GTV delineation protocol trials, (despite the time it took!).

Nuclear Medicine Department, the Wesley Hospital, Auchenflower, Brisbane. The

Nuclear Medicine Department provided scanning time and technical support for their

PET scanner and donated the radiopharmaceutical 18

F-FDG for the phantom

scanning. They also facilitated the PET scanning and data format requirements for

the patients whose images were used in this project.

Insight Oceania. Insight Oceania donated the use of the Syntegra software and the

import licences on the Pinnacle3

treatment planning system at the Mater Centre and

QUT for the conduct of this research.

I would particularly like to acknowledge the assistance and contributions of the

following people:

Personal acknowledgments

Dr Andrew Fielding: Principal supervisor

Senior Lecturer, School of Physical and Chemical Sciences, QUT

Thanks for your insights, patience and encouragement in bringing the different

aspects of this research together, and most importantly, for taking on the role of

supervisor in the last stages.

xvii

Associate Professor Dr Michael Poulsen: Associate supervisor

Director of the Mater Radiation Oncology Centre and Radiation Oncology QLD (St

Andrew’s Toowoomba)

The clinical aspect of this research would not have been possible without your

enthusiastic support as well as your guidance in focusing on the key clinical issues.

Robyn Guidi

Senior Radiation Therapist – Planning, Mater Radiation Oncology Centre

For assisting with the patient image registration pilot study and for facilitating the

use of the treatment planning system for the clinical trials. Your continued support

over the long period of time it took to complete the research will always be

appreciated.

Prue Raward and Mick Broun

Nuclear Medicine Technologists, Nuclear Medicine Department, Wesley Hospital

Thank you for your assistance with PET scanning the phantom (often at the end of a

long working day). Your support and ideas were invaluable in implementing the

clinical scanning protocols for radiation therapy patients.

Wendy Schumer

Radiation therapist and Applications Specialist, Insight Oceania

Thank you so much for your support, especially with the software and finding

solutions that enabled me to get this project under way. Your interest and

enthusiasm for research always results in great discussions.

Special acknowledgment to Chad Hargrave

I would not have submitted this thesis without your encouragement and support.

1

1

1.1 Introduction

Background

This project is essentially concerned with identifying and attempting to reduce errors,

using clinical protocols, which may be introduced when two different types of

medical images, computed tomography (CT) and positron emission tomography

(PET) are combined in the process of lung tumour definition in 3D conformal

radiotherapy (3DCRT). This process involves multiple steps starting with the

overlaying of the PET image onto a CT image in a radiotherapy treatment planning

system (TPS) and using the data from both images to define a volume on the images

which, as close as possible, equates to the extent of the cancer within the patient.

Radiotherapy aims to deliver a prescribed amount of radiation to a cancerous tumour

within a patient, requiring multiple daily treatment sessions often extending over a

period of many weeks. 3DCRT uses specific processes that allow a pre-determined

3D radiation dose distribution that conforms as tightly as possible to an individual

patient’s tumour to be precisely delivered while sparing as much healthy (or normal)

tissue from irradiation as possible. However, prior to any radiotherapy treatment

commencing, defining the dimensions and location of a tumour accurately within a

patient is essential and this is a significant component of the pre-treatment (or

treatment planning) process.

3D reconstructions of the internal and external anatomy of a patient using medical

images are an effective, non-invasive method of tumour localisation and definition.

An integral part of the 3DCRT process involves locating and defining tumours using

volumetric CT images. However, the sensitivity and specificity of CT scans in

imaging some malignant pathologies is one of the factors contributing to the

uncertainty of tumour volume definition in the treatment planning process using this

imaging modality.

Image registration and fusion software allows other imaging modalities with

improved sensitivity and specificity for certain tumours (compared to CT) to be

incorporated into the process of tumour volume definition. Image registration refers

2

to the process of translating and rotating one image to align with the anatomy on

another image. Image fusion refers to the process of visualising the registered

images1. Multi-modality image registration was implemented in most clinical sites

using magnetic resonance imaging (MRI) and CT images, most commonly for

enhancing the detection of brain tumours. This enabled the true extent of brain

tumours to be accurately visualised using the superior soft tissue contrast of MRI2, 3

.

The contrast between tissues of similar density such as those in the brain is superior

on MRI compared to CT. Verification of the correct alignment of registered MRI

and CT images is assisted by the fact that both CT and MRI produce high resolution

anatomical images.

PET is a nuclear medicine imaging technique which produces a 3D functional image

using various radiopharmaceuticals to assess different physiological functions of a

patient. Tumours with a high glucose metabolism can be detected using a glucose

compound labelled with radioactive fluorine, 18Flourine-2-flouro-2-deoxy-D-glucose

(18F-FDG). 18F-FDG PET images have demonstrated a higher sensitivity and

specificity for imaging certain primary tumours and any associated involved lymph

nodes compared to CT4

.

The use of registered CT and PET images to define tumour volumes during treatment

planning was hotly debated at the time this research project was commenced in late

2002. There was a lack of familiarity using and interpreting PET images in many

local radiotherapy departments at the time. Paulino and Johnstone’s article titled

“FDG-PET in radiotherapy treatment planning: Pandora’s box?”5

, is indicative that

the debate was still going strong in 2004. Published data was limited in answering

the specific issues relative to the incorporation of PET images into the radiotherapy

treatment planning process.

Generally speaking and particularly to the unfamiliar eye, PET images are almost

featureless backgrounds with some regions of intense uptake, and very little

anatomical information, making the prospect of accurate image registration with CT

difficult to achieve. There are various PET image formats, but initially there was

very limited data to suggest which was most appropriate to use in the registration

process. The issues involved in using CT/PET fusion in radiotherapy were

3

compounded by the introduction of automated image registration software. Up until

this point, all image registration in treatment planning had been performed manually

by the radiation therapist and the ability of the new automated software to accurately

register image data sets was still being proven. There were strong opinions that the

registered PET images should only be used to assist in the visual localisation of

tumours and involved lymph nodes and not for directly creating tumour volumes

based on their pixel data, due to image spatial resolution and issues surrounding

quantification of 18F-FDG uptake levels6

.

The use of CT/PET registration for tumour volume delineation for lung cancer was

chosen for this study as it was anticipated that investigations using image data of the

chest would incorporate many of the issues relevant to the use of PET in

radiotherapy treatment planning. At the start of this project there was a growing

body of published research which provided evidence that CT scans through the chest

and abdomen did not accurately image tumour volumes if they did not take into

account the respiration cycle of a patient7

. There were few studies on the effects of

respiration on PET imaging.

This research project did not seek to address the diagnostic accuracy of 18F-FDG

PET imaging. At the time questions existed and to some extent still exist regarding

the diagnostic accuracy of this type of imaging for differentiating between some

benign (such as inflammation or infection) and malignant pathologies8. During the

patient data acquisition phase of this project it was found that other diagnostic tests

are used to complement 18

F-FDG PET imaging findings when a diagnosis of lung

cancer is made in the clinical setting.

In the first stages of the project, considerable time was spent liaising with various

departments located in different hospitals to develop appropriate protocols for the

acquisition of the PET images that met patient positioning requirements for

radiotherapy treatment planning. Automated image registration software in

treatment planning systems was not widely available and had to be accessed. A

phantom which could be CT and PET scanned, with a component that could simulate

patient respiration, had to be designed and custom built. This first stage of the study

extended over a period of approximately two years. Limited access to PET scan

4

services (there was only one PET scanner in Queensland (QLD) at the time), along

with the high cost of a scan and limited Medicare funding eligibility impacted on the

time it took to acquire the images required for this research.

Since this research was begun, advances in PET technology have seen the

introduction of combined CT/PET scanners that provide “hardware” registered CT

and PET images. However, combined CT/PET scanning was not available clinically

in QLD until the beginning of 2006, which was after the completion of the data

acquisition phase. 4D or respiratory gated CT in radiotherapy has been developed to

eliminate imaging errors associated with imaging the chest and abdomen. This body

of research is presented in light of the technology clinically available during its

conduct. This said, all of the results presented in this study using a stand alone PET

scanner and pre-gated CT techniques can be applied to these new technologies and

techniques.

5

1.2 Literature review and background information

1.2.1 Guidelines for tumour volume definition in radiotherapy

A prescribed radiation dose for a given tumour is based on biological responses of

cancer cells to daily irradiation (or fractions) throughout a course of radiotherapy9

.

Failure to adequately treat a tumour has direct consequences on the ability to achieve

tumour regression and a cure. Tumour volume definition is the starting point for all

the procedures involved in the delivery of radiation therapy as it is the defined

tumour volume that is used to directly design the radiation treatment fields that are

used to deliver the prescribed radiation dose.

There are internationally recognised guidelines developed by the International

Commission on Radiation Units and Measurements10 11

• Gross Tumour Volume (GTV)

for tumour volume definition

in radiotherapy. These definitions provide guidelines that take into account various

patient and technology specific parameters that may impact on the accuracy of daily

coverage of a tumour volume by the treatment fields. These definitions (as quoted

from ICRU reports 50 and 62) are:

The GTV is the volume encompassing all visible malignant

disease on imaging studies (this includes both the primary and any

involved loco-regional lymph nodes).

• Clinical Target Volume (CTV)

The CTV is the volume that contains the GTV plus a margin that

includes subclinical microscopic malignant disease surrounding

the GTV.

• The Internal Margin (IM)

The volume that contains the CTV plus a margin to account for all

movements and variations in site, size and shape of the organs and

tissue contained in or adjacent to the CTV.

• Internal Target Volume (ITV)

The volume encompassing the CTV and IM which relates to the

CTV position in relation to internal and external reference points

preferably rigid anatomical features12.

6

• The Set-up Margin (SM)

To account specifically for uncertainties (inaccuracies and lack of

reproducibility) in patient positioning and alignment of the

therapeutic beams during treatment planning and through all

treatment sessions.

• Planning Target Volume (PTV)

Combines the GTV, and CTV, IM and SM margins. The PTV is

used to define the treatment field geometry.

1.2.2 Over view of the 3D conformal radiotherapy process

3DCRT is currently considered the international gold standard treatment technique

for delivery of external beam radiation therapy13. It aims to increase the therapeutic

ratio of radiotherapy (i.e. a cure with minimal side effects). Most normal tissues can

only tolerate radiation doses which are significantly lower than those required to

eliminate a tumour. Radiation tolerance levels for different tissues and organs are

based on the probability of a particular response to the treatment and its level of

severity (i.e radiation induced pneumonitis in normal lung tissue)14. For some

tissues, again using the lungs as an example, both dose and irradiated volume affect

the severity of side effects15. 3DCRT radiotherapy allows higher doses to be

delivered to a tumour whilst reducing dose to surrounding normal tissues by using

treatment fields that are designed to conform tightly to the shape of the PTV16

.

Unnecessary treatment of surrounding normal tissues due to excessive tumour

volume dimensions will increase patient side effects.

All radiotherapy treatments are divided into two main processes, treatment planning

and treatment delivery. The planning and treatment processes required for 3DCRT

are shown in Figure 1-1. Tumour volume definition in 3DCRT treatment planning is

based on a volumetric CT image acquired with the patient in their treatment

position17. Differences in patient positioning between their treatment position and

their scan acquisition position will result in differences in the patient’s tumour

position relative to their anatomy during treatment delivery. This in turns leads to

incorrect dosage to the PTV and the surrounding normal tissues.

7

Figure 1-1 The 3D conformal radiotherapy treatment process

Planning CT scan performed Patient is positioned as required for treatment delivery.

External marks are defined that will be used for accurate localisation of the treatment fields during treatment delivery.

Planning CT image imported into TPS External localising marks located and defined on CT image in TPS.

The tumour and any loco-regional lymph nodes are localised and defined (GTV definition)

Visualisation and contouring tools in TPS are used in this process.

Normal tissues are localised and defined Visualisation and contouring tools in TPS are used in this process.

Number of treatment fields and their geometry is determined A potential treatment field can be projected through a 3D reconstruction of the

patient’s planning CT scan to assess that it delivers the prescribed tumour (PTV) dose but avoids or limits the radiation dose to the normal tissues.

Radiation dose from all treatment fields is calculated A 3D dose distribution is displayed on the CT image of the patient.

Planned treatment is evaluated and approved The dose distribution to the defined tumour and normal tissues is evaluated.

The treatment plan is delivering prescribed radiation dose to the tumour and the normal tissues it is approved for delivery to the patient.

Treatment field geometry is exported to linear accelerator from TPS Treatment field parameters from approved treatment plan are imported at linear

accelerator where treatment will be delivered.

Treatment is delivered for the

number of specified

treatment days

Each day of treatment the

patient is positioned on the linear accelerator

treatment couch in the same position

that was used when their planning CT

was acquired.

The external localisation marks

defined at CT and on-line imaging

technology are used to determine accurate positioning of each

treatment field within the patient relative to

the defined location of the tumour on the planning CT scan.

The geometry of the treatment fields

designed to cover the tumour volume dimensions defined from the planning

CT scan are delivered for each

daily treatment

Tre

atm

ent p

lann

ing

Tre

atm

ent D

eliv

ery

8

The planning CT scan is imported into a radiotherapy treatment planning system

(TPS) by the treatment planning radiation therapist (RT). The radiation oncologist

(RO) uses the image viewing and contouring tools available in the TPS to localise

and define a patient’s GTV on the treatment planning CT image.

1.2.3 Lung cancer patients referred for 3D conformal radiotherapy

Radiotherapy can be used extensively for the treatment of lung cancer as a part of

initial treatment management or to treat progressive or recurrent disease18. The

decision to offer radiotherapy for lung cancer is based on the histology and stage of

the disease, incomplete surgical resection, the level of morbidity that may result and

the performance status of the patient. 3DCRT can be used to treat the following

groups of patients19 20

• SCLC which has responded well to chemotherapy.

:

• Medically unfit or elderly patients with Stage I and II non-small cell lung

cancer (NSCLC).

• Incomplete surgically resected Stage IIA and IIB NSCLC.

• Patients who have a good performance status for Stage III NSCLC.

These patients most often require treatment to primary tumours and locally involved

regional lymph nodes in the chest region21

.

1.2.4 Limitations of CT in GTV localisation for lung cancers

Accurate localisation of primary tumour volumes and loco-regional nodes using CT

images in radiotherapy treatment planning is limited by a number of factors. CT is

an anatomical image that relies upon recognition and discrimination of abnormal

densities and structure shapes to make an accurate diagnosis. Poor tumour definition

can occur if the tumour is the same density as the normal tissues surrounding it.

Lymph node diagnosis on CT relies on significant enlargement of the node (> 1.0 cm

in diameter)22

.

Three studies compared primary tumour and involved loco-regional node definition

(Van de Steene et al23, Giraud et al24, and Senan et al25). Large variations were seen

9

especially in the presence of atelectasis, with pleural reactions and neoplastic

parenchymal infiltrations which were noted to affect the process of disease

localisation. There was also a significant variation in the interpretation of positive

lymph nodes.

1.2.5 The sensitivity and specificity of PET versus CT for imaging

lung cancer

In Gambhir et al’s summary of PET literature4, the range of sensitivity, specificity

and accuracy of PET’s ability to diagnose malignant tumours across all histologies is

84-87%, 88-93% and 87-90% respectively. Coleman22

described the sensitivity,

specificity and accuracy of PET in primary mass diagnosis in lung cancer as ranging

from 82-100%, 75-100% and 79-94% respectively.

In the study by Fritscher-Ravens et al26 comparing CT, PET and endoscopic

ultrasound with or without fine needle aspiration, it was found that the sensitivity,

specificity and accuracy of PET to detect malignant nodes was 73%, 83% and 79%

respectively. CT had sensitivity, specificity and accuracy of 57%, 74% and 67%

respectively demonstrating PET’s superiority in involved node detection.

MacManus et al27

provided evidence that PET staging scans influence the

management of lung cancer patients, particularly impacting on the approach taken

for individual patient radiotherapy treatments.

1.2.6 The rationale of image fusion in treatment planning

Planning CT scans are not only used to visualise the tumour within the patient for

contouring purposes. 2D multi-planar reconstructions (MPRs) or 3D volumetric

reconstructions (see Figure 1-2) are used to virtually project and determine the

geometry of intended treatment fields to cover a given tumour volume via images

reconstructed from the CT scan28, 29. This is known as virtual simulation. Digitally

reconstructed radiographs (DRRs) are used to determine beam’s eye views (BEV’s)

of the geometry of each treatment field and its coverage of the PTV 30. DRRs and

BEVs are also used as reference images during treatment delivery to check the

accuracy of treatment field position within the patient.

10

Figure 1-2 2D and 3D images in a treatment planning system reconstructed from the planning CT (a) A transverse or axial 2D slice, (b) A MPR sagittal 2D slice, (c) A DRR with a BEV of an anterior treatment field conformed to the PTV (yellow), (d) A 3D render of the skin surface of the patient.

(a) (b)

(c) (d)

Human tissues are not uniformly dense and the density of these tissues affects the

absorption of radiation within the patient. There is a direct relationship between CT

numbers and electron densities of tissues. Treatment planning programs convert the

patient’s planning CT scan into tissue specific densities based on the linear

attenuation coefficient and then use this data to perform absorbed dose calculations

within the patient (see Figure 1-3)31. It is important that the planning CT scan is an

accurate representation of tissue heterogeneities to ensure accurate dose calculations.

It is possible to improve CT’s ability to image tumours using contrast media.

However, it is not desirable to use CT scans acquired with contrast in planning CT

scans. The density of the contrast media (which will not be present during the

patient’s treatment) will affect the accuracy of the dose calculations of the planned

treatment with respect to the delivered treatment32.

11

Figure 1-3 Dose distribution calculated using a planning CT scan for a lung cancer patient The PTV (solid yellow contour), the planned treatment field coverage of the PTV and the calculated dose distribution are shown relative to the patient’s planning CT scan. The planned treatment fields are an anterior (red), a left anterior oblique (blue), a left posterior oblique (yellow), and a posterior (green). The dose distribution in the patient is indicated by the isodose lines for different levels of absorbed dose, measured in Gray (Gy).

Radiotherapy treatment planning systems have image registration and fusion

software options. Image registration and fusion allows other imaging modalities

which are more sensitive and specific for imaging the full extent of a patient’s cancer

(a contrast enhanced CT, MRI or PET scan for example) to be incorporated into and

improve the accuracy of the GTV definition process in 3DCRT33, 34. Image fusion

software visualisation tools (see Figure 1-4) enable the unique or complementary

information from the secondary image to be used in conjunction with the planning

CT scan to localise and contour the GTV35

. The planning CT scan is still used as the

primary image data set for treatment field visualisation and localisation, and for dose

calculations.

12

Figure 1-4 Image fusion software visualisation tools (a) From left to right: transverse, sagittal and coronal MPRs of the planning CT scan of a patient with a brain tumour. (b) An MRI scan of the same patient demonstrating the tumour in the right parietal lobe of their brain. A look up table with a different colour scale has been applied to this image. (c) Different displays of the fused images allowing for the better imaged dimensions of the tumour on the MRI to be localised on the planning CT scan. From left to right: checker box display, blended display, and cut-away moving box display.

(a)

(b)

(c)

13

1.2.7 The potential of CT/PET fusion to improve GTV definition

accuracy for lung cancers

It has been reported from clinical trials that GTVs are frequently altered by the ROs

when CT-PET fusion has been performed on lung patients. Erdi et al36 performed a

study on the use of PET-CT fusion to provide enhanced GTV and involved node

definition in the 3D planning of lung cancer patients. They found that for all

patients, the PTV (derived from combining the involved nodes with the GTV)

delineated from CT alone changed when the PET image was registered. PET

reduced the PTVs in two patients due to discernment of the tumour within atelectasis

while for other patients it detected small, involved nodes that would not be

considered malignant with CT alone. The results of a similar study by Mah et al37

reflected the same findings. PET-CT registration was found to identify positive

nodes that were not considered malignant on CT as well as enhancing tumour

definition for poorly defined tumours.

1.2.8 Potential errors associated with incorporating registered

CT/PET images into the GTV definition process for lung

cancers

High levels of accuracy are required throughout all the processes involved in

delivery of radiation therapy. The margins applied to achieve a PTV are required

due to the inherent geometrical uncertainties associated with localising any point in a

patient on any given day. Both patient physiological factors and precision levels of

the technology used in radiotherapy treatment planning and delivery will impact on

these geometrical uncertainties38

.

Van Herk39

• A systematic error is the result of any variation during treatment

preparation which would lead to an initial displacement of the dose

derived a methodology and formula for deriving margins to account for

geometric uncertainties in both the planning and treatment processes. This formula

is based on the standard deviation of systematic and random variations (or errors) in

the delivery of planned treatment fields to a tumour. These two classes of errors can

be defined as:

14

distribution relative to the CTV. A systematic error would be delivered

for all treatments.

• A random error is the result of variation during treatment execution, and

its magnitude will vary for each fraction of the patient’s treatment.39, 40

Based on these definitions any uncertainties or errors that may be introduced when

using registered PET and CT images for contouring a GTV in the 3DCRT process

would be classed as systematic. Therefore the processes involved in acquiring,

registering and contouring of the CT and PET images need to be examined, as any

errors in this process would be continued throughout the entire course of a patient’s

treatment. It is also important to reduce the level of any systematic errors where

possible so that optimal margins can be applied to a CTV. Optimal margins for

radiotherapy of lung cancers are those that ensure adequate CTV coverage despite

uncertainties in GTV localisation while treating the smallest possible healthy lung

volume.

Armstrong41

highlighted multiple factors relating to technology and techniques in the

treatment planning process that could impact on accurate target volume definition for

3DCRT of lung cancer when using CT images alone. Image acquisition techniques,

image quality, accurate patient positioning in their treatment position and tumour

motion will directly impact on the accuracy of using the planning CT scan to target a

tumour for daily radiotherapy treatment. These image acquisition factors also

translate to PET images that will be used in the GTV definition process.

The use of more than one imaging modality through image registration can increase

the geometrical uncertainty associated with contouring using these images42. During

the image registration process it is important to ensure that the same point in the

patient is aligned on the two images so as not to introduce an offset error in

localisation and definition when the tumour volume is defined on the secondary

image. Accurate image registration can be affected by the method used to register

the images and inter-user variability when registration results are assessed43

.

Inter-observer variability between the ROs’ contouring results in GTV definition is

another source of geometrical uncertainty that needs to be considered42. The aim of

15

incorporating PET image data into the 3DCRT treatment planning process is to

reduce inter-user variations due to difficulty in ascribing CT densities to pathology.

Previously mentioned lung cancer GTV definition studies 23-25

• Training and individual experience in image interpretation

also identified the

following factors as affecting contouring accuracy:

• Problems with the contouring methodology in the TPS.

1.2.9 Planning CT and 18

Image quality is important when defining and localising structures for 3DCRT

treatment planning. The contrast and spatial resolution of the images affects the

ability to differentiate between different tissues as distinctly different objects. Partial

volume and patient motion artifacts can also lead to structure misrepresentation and

hence incorrect definition. Image voxel size is an important feature when

considering image quality, particularly partial volume effects.

F-FDG PET image acquisition and quality

1.2.9.1 Planning CT image acquisition

There are many CT scan parameters that, depending on those selected prior to

acquisition, will affect image quality. The intended use of the resulting image will

determine actual scan parameters for each patient’s scan. Desirable qualities for

planning CT scans are:

• The patient must be scanned in their treatment position to avoid tumour and

critical tissue localisation errors.

• The entire patient’s external contours, the couch and any relevant stabilisation

equipment should be imaged. This is essential for calculating dose

absorption within the patient and the linear accelerator monitor units for each

treatment field.

• The volume of the patient imaged should include sufficient anatomy for

accurate treatment field localisation and dose volume calculations.

• High quality axial (transverse) images with high contrast resolution between

tissues and minimal partial voluming artifacts especially on sagittal and

coronal multi-planar reconstructed images.

• High spatial resolution DRR images.

16

• Minimal artifacts from patient movement during scanning.

Radiotherapy departments routinely use multi-slice CT scanners dedicated to

acquiring planning CT scans for 3DCRT treatment planning. These scanners have a

flat couch top to which a solid carbon fibre panel which replicates those that form

parts of the treatment couch on the linear accelerator can be added. A system of

positioning lasers are mounted in the scanner room to assist with localising external

reference marks on the patient that will be used to localise the treatment fields within

the patient. Wide-bore multi-slice CT (MSCT) scanners with diameters up to 85cm

have been developed to accommodate the stabilisation equipment and patient

positions required for radiotherapy17, 44

. The Siemens Somatom Open Sensation,

used for acquiring the CT images for this research, is a 20 slice, wide-bore scanner.

Scanning parameters can be selected by the RT in a similar fashion to diagnostic CT

imaging; however certain planning CT scan imaging parameters are only selected

using pre-defined protocols. Image reconstruction algorithms and filters and kV are

determined during the commissioning phase of the CT scanner when the CT number

to electron density conversion is calibrated45. These parameters are never altered as

this will affect the accuracy of dose calculations within the treatment planning

system. Automatic exposure controls (AECs) are routinely used in planning CT scan

acquisition to reduce patient dose. Using AEC software the mAs is automatically

determined by the scanner from the topogram (z-axis modulation) and a priori

feedback (angular modulation) during the scan acquisition46, 47

.

Fast acquisition times are desirable for planning CT scans to reduce artifacts

introduced by patient movements on the couch top during acquisition. Increasing

pitch can allow larger volumes of the patient to be imaged quickly. However this

can introduce geometrical distortions and result in poor z-axis resolution in the

images48, 49

. A smaller pitch will increase patient radiation exposure. Optimal pitch

for imaging is chosen to give maximum scan speed, image quality and minimal

radiation dose. For planning CT scans, pitch is routinely selected from the default

values in region specific protocols which were determined during the commissioning

stage of the CT scanner.

17

The field of view (FOV) and slice thickness are two scanning parameters that are

regularly modified for individual patient planning CT scans. Wide-bore MSCT

scanners such as the Siemens Somatom Open Sensation not only have a large

aperture to accommodate stabilisation equipment and radiotherapy patient

positioning, but also have extended FOVs to enable complete imaging of the two.

This scanner can use its extended FOV to reconstruct images up to 82 cm in

diameter50

. Pixel size is influenced by the FOV. The smaller the imaged FOV the

smaller the pixel size of the image will be, resulting in higher image spatial

resolution. The smallest possible FOV that will include the entire patient’s external

contours, the couch and any relevant stabilisation equipment is selected for planning

CT scans.

The z-axis of a voxel in a CT image equals the image slice thickness. The slice

thickness used for a particular examination is determined by the size of the object

being imaged. For example, imaging of nodes or small lesions requires 2-3 mm

thick slices to ensure adequate sampling and to minimise partial volume effects

(demonstrated in Figure 1-5), whereas imaging the chest or liver only requires 8 mm

slices51. Slice thickness is also important in CT acquisition to generate DRRs with

high spatial resolution. The spatial resolution of DRRs is primarily limited by the

voxel size of the CT data52

.

Figure 1-5 Partial volume effects caused by thick CT slices The images below are from left to right, a transverse, a sagittal and a coronal slice of a planning CT data set. The arrows on the images indicate the different image axes relative to the patient. Yellow = the x axis (the left/right dimensions of the patient). Green = the y axis (the anterior/posterior dimensions). Red = the z axis (superior/inferior dimensions). When large slice thicknesses are selected (3cm for this scan) partial volume effects are most noticeably seen in the z axis of the image, resulting in poor z axis and DRR spatial resolution

18

1.2.9.2 PET image acquisition and reconstruction

PET images are achieved by labelling a pharmaceutical with positron emitting

radioisotopes; the pharmaceutical is then administered to the patient either

intravenously or by inhalation. Two 511 keV photons emitted at 180 degrees from

each other are the result of the annihilation process of an emitted positron. The

energy of these photons is sufficient to allow most of them to pass through the body

of the patient53

.

A PET scanner consists of a circular array of detectors. Each detector is able to form

a coincidence line with any one of the opposing detectors. A detector can form

multiple coincidence lines with detectors across from it, forming a fan-beam

response, or at any given angle, such that a series of parallel coincidences are

formed. The positional information of the positron emitter is achieved by recording a

large number of coincidence events. The location and direction of any given

coincidence line is unique. This information is stored as 2-D matrices called

sinograms which are used to reconstruct a cross-sectional tomographic image53

.

There are many factors affecting PET image quality and detection sensitivity, most

of which cannot be altered during scan acquisition as they are intrinsic to the

radiopharmaceutical properties, scanner design or the object being imaged. PET

detector design (size and spacing)54, positron range prior to annihilation, and the non-

colinearity effect seen in the two 511keV photons will all affect PET image spatial

resolution55. Detector composition, size, position and number, and the imaged voxel

size, affect overall detection sensitivity. The voxel size of PET images is inherent to

the detector design and the image reconstruction algorithm used. There is reduced

image sensitivity in detecting objects that equal to or smaller than the detector

resolution or smaller than the image voxel size due to count recovery losses56.

Objects equal in diameter to the PET scanner resolution have been shown to have a

recovery coefficient of 31.6%57

. The recovery coefficient is a ratio of the measured

activity in an object in the PET image divided by its true activity.

If an imaged object is partially within a voxel then both axial and transverse partial

voluming effects can be seen. Regular shaped objects (large, circular and widely

spaced) are less likely to suffer partial voluming effects than irregularly shaped

19

objects (small, thin and close together)58. The ratio of activity in a volume of

interest to the activity surrounding it, termed the target-to-background ratio (TBR),

affects the contrast resolution of PET images59. Modifying the scan acquisition times

and injected activity (PET imaging parameters which can be altered) can increase

contrast-to-noise ratios, increasing detection sensitivity59. Clinical scanning times

and activity concentrations are tested and evaluated during PET scanner performance

tests60

.

It is recommended that PET imaging studies for lung cancer diagnosis use a 3D

whole body attenuation-corrected image acquisition protocol. Small lesions and

those deep within the body are more accurately detected using this scanning

protocol22. Whole body PET involves imaging the patient in sections (called

frames), the length of which depends on the dimensions and number of detector

rows. A table moves the patient through the scanner as each frame is acquired. The

image can be acquired in 2D or 3D mode. 2D mode uses inter-planar tungsten septa

between detector rows in the axial (z axis) direction of the scan, limiting coincidence

detection to the transaxial (x and y) directions. 3D mode scans without the septa

allows for coincidence detection from all three planes, which can increase image

sensitivity by a factor of 4-653

.

Non-uniform absorption of some of the photons within the patient may produce

image distortions such as regional non-uniformities, intense objects and edge

effects61. The effects can be caused by the high density of the skull or low density of

the lungs, affecting the absorption of emissions and can be corrected for by

determining the probability of attenuation for all sources along a given line of

response by using the dimensions and densities of a transmission scan (see Figure 1-

6). Small objects are also more accurately imaged with attenuation corrected

images62

. An attenuation corrected (AC) emission scan is the result of applying

attenuation correction probabilities obtained from the transmission scan dimensions

on the stored data of the emission scan.

20

Figure 1-6 Comparison of a patient’s non-attenuation corrected and an attenuation-corrected emission scan (a) The emission scan of a patient without attenuation correction: note the darkness of the image in the mediastinum and the skin flaring. (b)The AC emission scan: note the improved resolution and the change in the size of the tumour.

(a) (b)

The transmission scan can be achieved using either a gamma source (usually 68Ge or 137Cs) or a CT scan depending on the type of scanner configuration55. Stand alone

PET scanners use a rotating single gamma source to acquire the transmission scan

simultaneously with each emission frame. Transmission scans are performed with the

start point above the first emission frame and below the last emission frame.

CT/PET scanners consist of a CT and PET scanner combined with a single couch

assembly. A CT scan is performed as a separate scanning protocol just prior to the

PET emission scan and is then used for applying attenuation correction to the

emission scan once it is completed. The Philips Allegro PET scanner used for

acquiring the PET images for this research is a stand alone PET scanner with a 137Cs

gamma source for acquiring transmission scans60

. Transmission scans acquired with

a gamma source have poor contrast resolution compared to CT/PET scanner

transmission scans acquired using the CT component of the scanner (see Figure 1-7).

21

Figure 1-7 Image characteristics of PET emission, transmission and attenuation-corrected emission scans (a) PET emission scan (b) Gamma source transmission scan (c) CT transmission scan (d) AC emission scan

(a) (b) (c) (d)

1.2.9.3 PET image quantification

A PET scan is essentially an image of emitted annihilation photons, from within the

object, counted by the detectors. PET scans can also be quantified by using a

standardised uptake value (SUV). The SUV in 18F-FDG imaging is used as a tool for

indicating malignancy. At its most basic the SUV is the measured concentration of

FDG in an image normalised to total injected activity and patient weight as

demonstrated in the equation below63

.

t(g)body weigh / (Bq) dose injected(Bq/g)ion concentrat tissue SUV =

The use of body surface area for normalisation has been shown to provide a more

robust SUV value than body weight64. There are other methods of determining SUV

such as kinetic models that require blood samples midway through the scan

acquisition65. There has been much debate over the application of SUVs in

quantifying a PET image due to many sources of variability such as patient size and

body composition, differing post-injection scanning times, patient glucose levels, and

the effects of the dimensions of a lesion on image recovery coefficients and partial

volume effects6

.

22

1.2.9.4 The effects of motion on CT and PET image quality

Patient respiration causes not only the displacement of the diaphragm and the chest

wall but also tumours and lymph nodes. The displacement between the inhale and

exhale position of a tumour is referred to as the tumour’s range of motion66.

Seppenwoolde et al’s67

real-time study measured the 3D range of motion for a group

of 20 patients, tracking implanted gold seed markers using fluoroscopic imaging

during treatment. The range of motion in the superior to inferior direction of the

patients was 12 mm ± 6 mm for tumours in the lower lobes and those not attached to

rigid structures, or 2 mm ± 2 mm for upper lobe tumours or those attached to rigid

structures. The range of tumour motion in the left-to-right and anterior-to-posterior

directions is significantly less (1.2 mm ± 0.9 mm and 2.2 mm ± 1.9 mm

respectively).

The time it takes image the whole chest with a Siemens Somatom Open Sensation

scanner is less than 10 seconds. On average a patient’s breathing cycle (from

inspiration to expiration) is 3 – 4 seconds67, 68, resulting in a respiration rate of 15 to

20 cycles per minute. The imaged dimensions of any structure or tumour under the

influence of respiration are dependent on the position of the volume of interest

relative to the timescale of the acquired images. Without breath hold techniques or

coordination of scan acquisition times with the patient’s respiratory cycle, respiration

induced motion artifacts on CT will occur. These artifacts are typically demonstrated

as incomplete imaging of structures, such as the diaphragm, when a patient breathes

freely during scan acquisition (see Figure 1-8). Tumour volume dimensions are not

accurately imaged and neither is their full range of motion within the patient

captured7

.

23

Figure 1-8 Motion artifacts on a free-breathing planning CT scan of the chest The sagittal (a) and the coronal (b) images of this patient have motion artifacts demonstrated as the irregular imaging of the surface of the diaphragm.

(a) (b)

Data collection and reconstruction techniques for 4D CT and respiratory gated CT

are synchronised with a patient’s respiratory cycle. 4D CT images significantly

increase the ability to accurately image both the dimensions and the full range of

motion of a tumour volume compared to free-breathing scans66. 4D CT results in

multiple image data sets, each representing a phase of the patient’s respiratory

cycle69. A respiratory gated scan images a particular portion of the patient’s

breathing cycle and hence only one image data set is obtained using this method66

.

4D or respiratory gated CT images are registered with a free-breathing planning CT

scan. 4D and respiratory gated CT scanning protocols are becoming more widely

implemented. However it is standard clinical practice to also acquire a free breathing

planning CT scan when using 4D CT images. This allows for tumour volume

definition using the respiration correlated images, while dose calculations are based

on the free-breathing scan.

Respiratory motion artifacts can occur for all imaging modalities66. There have been

investigations into the effects of motion on PET scans. It is felt that due to the

slower scan acquisition times for stand-alone PET scanners that the full positional

range of structures and tumours will be imaged. The recommended acquisition time

per emission frame for the Allegro PET scanner is 3 minutes. Each transmission

frame using the 137Cs takes approximately 2 minutes60. These acquisition times are

many times greater than a patient’s breathing cycle (e.g. approximately 45 – 60

cycles per emission frame) producing a time-averaged image70.

24

1.2.9.5 The implications of clinical PET scan access for patient positioning

Planning CT scans of lung cancer patients are performed with the patient positioned

supine with either their arms up above their head or down by their sides depending

on the location of the GTV. The majority are scanned with their arms up to avoid

irradiation of their arms during treatment. Patients with GTVs in the apex of the

lung will be treated with their arms down for the same reason. All of these patients

will require some form of head and neck support to keep their chin out of the way of

any treatment fields. The range of the CT scan is usually from below their chin to

below the diaphragm to ensure that the entire lung volume is included in the scan.

For whole-body PET scanning, patients are scanned from the level of their external

acoustic meatuses (EAMs) to the mid thigh region. Scan times can range from 20-30

minutes depending on patient height. Therefore a comfortable position in which the

patient is least likely to move is chosen. Typically the patient is positioned supine on

a curved couch top with a mattress, with a pillow under their head with their arms at

their sides. Their arms may be positioned above their head if necessary to fit their

shoulder region through the scanner. The diameters of PET scanner apertures are

significantly smaller than those of CT scanners.

It is essential that if a PET scan is to be used for image registration with the planning

CT that the patient should be in the same position as their treatment position for both

scans. The Erdi et al36 CT/PET fusion study makes specific mention of efforts to

limit tumour localisation errors by ensuring that patients were all imaged in the same

position for PET and CT. Several studies have highlighted that one of the main

problems in accurately registering whole body diagnostic PET images with planning

CT scans is differences in patient positioning. Using a flat couch top insert and

radiotherapy stabilisation equipment for the diagnostic and staging PET scan is

therefore recommended34, 36, 37, 71

.

Access to PET imaging in Australia is restricted by the availability of PET scanners

and the eligibility of patients for Medicare funding. During the image acquisition

phase of this research (2004 – 2005), there was only one PET scanner in QLD. This

scanner was situated off-campus to all but one radiotherapy department in the state.

The Medicare Benefits Schedule is a listing of the Medicare services subsidised by

25

the Australian government. For lung cancer patients, funded PET imaging is limited

to whole-body staging PET scans. Table 1-1 lists the Medicare funded criteria and

codes for whole body PET for lung cancer patients during the patient image data

acquisition phase for this study 72

.

Table 1-1 Medicare funded criteria and codes for whole body PET for lung cancer (November 2004)72

Code Purpose

61523

Whole body FDG PET study, performed for evaluation of a solitary pulmonary nodule where the lesion is considered unsuitable for transthoracic fine needle aspiration biopsy, or for which an attempt at pathological characterisation has failed.

61529 Whole body FDG PET study, performed for the primary staging of proven non-small cell lung cancer, where curative surgery or radiotherapy is planned.

Generally the whole body PET for diagnosis and staging of lung cancer patients is

the only scan available for use in their treatment planning process. As yet there is no

Medicare rebate for a PET imaging performed solely for the purpose of radiotherapy

treatment planning. This has implications for acquiring PET scans for radiation

therapy purposes. If the staging PET scan is performed without consideration of the

patient’s radiotherapy treatment position, the accuracy of registering this image with

the planning CT scan for the purpose of GTV definition is compromised.

1.2.10 Image viewing and interpretation

CT has good contrast and spatial resolution as well as high geometrical accuracy

making visual identification of the boundaries of distinctly different anatomical

features possible. Visualisation of CT images is based on greyscale conversion of

CT numbers. CT numbers are converted to a 256 level grey scale for viewing.

Window widths and levels are used to determine how the grey scale is applied to the

CT numbers73

• Window level (WL) determines the mid-range CT number.

. Window level and width are defined as:

26

• Window width (WW) determines the upper and lower range of CT numbers

to be viewed.

Pixels above the upper value are viewed as white while pixels

below the lower range are display as black.

The range of CT numbers between the upper and lower limit are

displayed in the greyscale range

Appropriate window levels are essential for viewing structures of different densities, as shown in Figure 1-9. Figure 1-9 Lung and mediastinal CT viewing windows for the chest region (a) Lung viewing windows used for visualising tumours in the lung tissue (WL=-300, WW=1300). (b) Mediastinum viewing windows used for visualising involved lymph nodes (WL=800, WW=400)

(a) (b)

For PET image interpretation the key consideration is that 18F-FDG PET images

glucose metabolism (i.e., the uptake of glucose by tissues within the body). There is 18F-FDG uptake in normal tissues due to their glucose metabolism74. It is essential to

understand the expected levels of uptake in the various normal tissues, as this can

assist with differentiating between normal structures, tumours, involved lymph nodes

or metastatic disease in the patient. The expected levels of 18

• High uptake in the brain, ureters, kidneys, bladder, active muscle, heart (fed

state), the left ventricle of the heart in some patients and the bowel.

F-FDG in normal

tissues are:

27

• Moderate uptake in active bone marrow (typically seen in the vertebral bodies

and sternum of adults), the liver and lactating breasts or breast implants.

• Low uptake in the bowel, resting muscles and the heart (fasted state)

PET studies are usually performed with the patient in a fasting state so that expected

normal tissue uptake occurs. Normal tissue or background uptake can vary from one

patient to the next due to individual patient metabolism. Hyperglycaemia and

hypoglycaemia affect the uptake of 18F-FDG throughout the body. Hyperglycaemia

results in less 18F-FDG uptake as the pharmaceutical competes with blood glucose.

Hypoglycaemia results in higher uptake in normal muscle tissue and reduced uptake

in tumours. The effect of either of these conditions is to reduce image contrast,

making it harder to distinguish malignant tumours75. When using 18F-FDG PET

images to locate malignant disease an awareness of high uptake levels relating to

benign pathologies, such as inflammation, is important. Nuclear medicine

radiologist guidance on image interpretation is recommended when PET images are

to be used in radiotherapy treatment planning5

.

Qualitatively, uptake within a lesion can be compared with the uptake of the brain

because as the brain metabolises only glucose it is expected that it will have the

highest possible level of uptake. Tumours have a high glucose metabolic rate so

uptake in the brain can be used a reference by which uptake in tumours can be

compared75, 76. Window levels will be relative to the voxel with the highest

measured 18F-FDG uptake for PET AC images. Therefore the grey scale levels used

for windowing for a PET image typically show the brain as the brightest part of the

image with all other regions scaled back from this level, with black indicating no

uptake. Due to this relative scaling, selecting different window widths and levels

will result in different visual sizes of a volume of interest on a PET image (see

Figure 1-10). Therefore reliable window thresholding needs to be developed for

viewing PET images for contouring GTVs34

.

28

Figure 1-10 Effect of different window levels on apparent dimensions of a tumour The two images below demonstrate the effect of different window levels for PET images on the visual size of a tumour. The PET images have been registered and fused with the corresponding patient CT data.

1.2.11 Image registration techniques

Images are classed as either the primary data set or secondary data sets for the image

registration process. In radiotherapy treatment planning, the planning CT scan is

conventionally defined as the primary data set. Image registration at its most basic

involves the translation and rotation of a secondary data set to align corresponding

patient features with the primary image. The primary image remains fixed, while

there are 6 degrees of freedom (3 translations and 3 rotations about the 3 Cartesian

axes of the image) that can be applied to the secondary data set77. Figure 1-11

demonstrates the direction of the translation and rotations of the secondary image

about the image axes relative to the patient. The transformation of the secondary

image onto the primary image may be performed using different registration

methods, broadly classed as manual, semi-automatic, or automatic techniques1, 78

.

29

Figure 1-11 The direction of the translation and rotations of the secondary image about the image axes relative to the patient The arrows on the images indicate the direction of translation and rotation of image axes relative to the patient. Yellow = the x axis translations and rotations, green = the y axis translation and rotations, and red = the z axis translations

1.2.11.1 Manual registration

In manual image registration, the user interactively translates and rotates the

secondary image until it is aligned with the primary image. This method of

registration relies on image quality and the user to identify and match features on

both data sets. Of all the techniques this method is the most dependent on the skill

and experience of the user in order to obtain accurate registration43

.

1.2.11.2 Semi-automatic registration techniques

Semi-automatic techniques rely on the user to identify surfaces or landmarks on both

image sets. There are two approaches to using this method of registration. Either

contoured anatomical surfaces or anatomical points and markers are used to form the

basis of the registration79. Once the contours or points are defined separately on each

patient image an algorithm is used to automatically register the images based on the

defined contours or the points. Inaccurate definition of features prior to registration

will affect the accuracy of the registration outcomes. Contour or point-based semi-

automatic registration requires the image to have high resolution so that features are

able to be clearly identified by the user43

.

A described method of semi-automatic feature-based registration uses the chamfer

matching algorithm to automatically register the images based on the lung surface

30

contours80. Contour matching algorithms are most likely to fail if natural symmetries

exist in the contours which make multiple registration solutions possible. Figure 1-

12 demonstrates that while registration may appear to be achieved in one plane (the

transverse alignment as shown in Figure 1-12A) true registration may not occur in

other planes. Due to the symmetrical geometry of the feature the algorithm can find

multiple solutions in another plane (Figure 1-12B or C) 81

.

Figure 1-12 Example of multiple solutions for contour-based registration For an object with natural symmetries, correct alignment of two images may be demonstrated in one plane (the transverse view shown in A), but may or may not be correctly aligned in other planes (as shown in B and C)

Point-based registration involves identifying the same point, relative to the patient,

on the two images. As it provides fewer match points it is a more efficient form of

registration but does require a minimum of three non-colinear points to reduce the

possibility of multiple solutions79. The points chosen can be intrinsic (small

anatomical features) or extrinsic, such as fiducial markers. Locating small discrete

anatomical features on a PET image is limited by the low resolution of these

images43

. Fiducial markers can provide a means of overcoming this problem.

Fiducial markers are routinely utilised in radiotherapy for localising set-up reference

marks on the patient’s skin surface on their planning CT scan. CT fiducial markers

are radio-opaque and are typically made from thin wire or a small ball bearing.

These are placed over external localisation marks (such as tattoos) on the patient’s

Transverse plane show

n in A

31

skin at the time of their planning CT scan. Once the patient field geometry has been

finalised in the TPS these fiducial markers are identified on the planning CT scan

and are then used to determine set-up instructions for correct treatment field

positioning within the patient relative to the patient’s external localisation marks17

.

The size of a fiducial marker is important and the size chosen is generally dependant

on the spatial resolution of the scanner and the voxel size of the image. If the

fiducial marker is smaller than the spatial resolution of the scanner then it may be too

small to be seen on the image. Anything larger than the size of a voxel will result in

an increase in partial voluming effects of the fiducial marker. The relationship

between slice thickness and CT fiducial marker size is important to reduce marker

localisation errors on the planning CT scan. The positional error in the z-axis

location of the patient’s tattoo on CT is equal to the slice thickness if the fiducial is

imaged over two consecutive slices. If it is imaged over three slices the error is up to

twice the slice thickness (see Figure 1-13).

Figure 1-13 The potential error in fiducial marker localisation as a factor of slice thickness and marker size The blue fiducial marker in (a) is just smaller than the slice thickness and will only be imaged on slice 2. The orange marker in (a) is the same as the blue marker but its centre is directly over where the edges of slices 4 and 5 meet, therefore it will be imaged on both slice 4 and 5. The blue fiducial marker in (b) is slightly larger than the slice thickness and while it is centred over slice 2 it will be imaged over slices 1-3. The orange marker in (b) is the same size as the blue marker but will only be imaged over slices 5 and 6.

(a) (b)

32

A technique called “zeroing the couch” when CT scanning can assist in reducing the

positional localisation of a fiducial marker as a result of partial voluming effects.

This technique involves aligning lasers with the centre of the fiducial marker

visually. The longitudinal or Z value of the CT couch is re-calibrated to 0 where the

laser coincides with the couch. The z value of all the other CT slices in the scan will

be relative to this “zeroed” slice.

Studies utilising fiducial markers for point-based PET-CT registration have used

identical markers during the acquisition of both the PET and CT scans, requiring the

marker to be both radio-opaque and PET avid82. Therefore the fiducial markers need

to be able to contain the PET radiopharmaceutical and also an optimal size so that

they can be imaged by the PET scanner (i.e. not smaller than the resolution of the

detectors) while limiting partial voluming effects. Partial voluming effects can

impact on the ability to accurately determine the centre of the marker on images

which in turn can affect the accuracy of the point-based registration results83

.

1.2.11.3 Automatic registration techniques

Automatic image registration techniques use algorithms to perform registration based

on feature extraction84 or statistical analysis of the voxel intensities of the two image

data sets79. These techniques do not require points or surfaces to be identified

interactively by the user prior to registration. However even using automatic

processes, accurate feature extraction can be limited for low resolution and contrast

nuclear medicine images. Alternatively automatic image registration based solely on

the voxel intensities of the two image data sets eliminates any interactive or

intermediate steps in the registration process that can result in misalignment of the

images1, 78, 79

.

Voxel-based automatic image registration uses a similarity measure to evaluate the

alignment of the secondary image with the primary image. For a given

transformation (i.e. a set of translations and rotations) of the secondary image onto

the primary image, the similarity measure is calculated using the intensity level

information from both images. These calculations are most commonly performed

using only the overlapping data from the two images81. The two most common

33

similarity measures used in automated registration are cross-correlation (CC) and

mutual information (MI)85, 86. Both of these similarity measures can be selected for

use with the voxel-based automatic image registration software in the Syntegra

platform of the Pinnacle3 TPS87

Robust image registration using the CC similarity measure requires a linear

relationship between the grey scales of the two image data sets and is most often

used for registering intra-modality images (e.g. CT/CT or PET/PET registration).

The actual intensity values from each image are used to calculate the CC similarity

measure as shown in the following formula

.

81

.

−

=

∑ ∑

∑

Ω∈ Ω∈

Ω∈−

TBAA T

BAAAA

TBAA AA

X X X )(X

X XX

B - B)A (A

B - BAA

, ,

,

2)(

2

)( )(

)(

)( )( CC

τ

τ

where: A = Image A (primary image)

B = Image B (secondary image)

T = a given transformation or spatial mapping of image B onto A

AX = a voxel location within image A

)( AXA = the intensity value for a voxel at the location AX

B AXτ

)( = The intensity value for a voxel within image B relative to its

mapped position onto image A for a particular transformation T

BAAX ,Ω∈ = For all the voxel locations relative to image A in the overlap regions

of images A and B for a given transformation

A = The mean of the voxel intensities from image A within the overlap

domain of images A and B

B = The mean of the voxel intensities from image A within the overlap

domain of images A and B

The closer the CC similarity measure is to a value of 1.0 for a given transformation

of the secondary image onto the primary, the more likely it is that the images are

correctly registered.

34

The MI similarity measure is based on the probability distributions of the grey values

in each image85, 86, 88, 89. It does not rely on an explicit relationship between the grey

scales of the two images being registered (as does the CC technique), which makes

this similarity measure more appropriate for inter-modality image registration (e.g.

PET/CT registration). Essentially, the joint entropy of the probability distribution of

the intensity levels for both images is determined by the MI similarity measure. The

greater the reduction in the joint entropy of both images, the higher the probability

that a given transformation results in a true registration of both images88. The

Pinnacle3 TPS uses a normalised MI similarity measure as expressed by the

following equation87

.

∑

=

j,kfk

rj

Dj,k

Dj,k

PPP

VP 22

log- MI

where: V = the volume of overlap between the images

Prj and P f

k = the probabilities of grey value j and k in the reference and

secondary image respectively

P Dj,k2 = the probability that grey values j and k occur in the reference

image and at the corresponding position of the secondary

image.

There are a number of factors affecting the accuracy of voxel-based registration. As

discussed previously, different similarity measures are more suited to intra or inter-

modality image registration. Selecting the CC similarity measure to register a CT

and a PET image is inappropriate as there is no direct relationship between the

greyscales of the different features in these images. Other factors affecting the

robustness of automatic voxel-based registration are the techniques utilised by the

image registration software and image attributes.

When describing image registration processes it is important to note that

transformations can be either global or local. Global transformations are applied to

the entire secondary image: this is referred to as rigid registration1, 43. Local

transformations are used in deformable (or elastic) registration where different

35

rotations and translations are applied to sections of the secondary image. This warps

the secondary image to achieve a more accurate mapping of anatomical features from

this image onto the primary image. Deformable registration is useful when the

spatial relationships between anatomical features on the two images are not the same.

This may be due to the patient not being placed in exactly the same position when

each image is acquired or physiological factors such as respiration90, 91

.

Identical patient positioning for the planning CT scan and the whole-body staging

PET scan for accurate image registration has been previously discussed (see Section

1.2.9.5). Deformable registration software is not currently available in clinical

radiotherapy treatment planning systems such as the Pinnacle3 TPS. In the absence

of deformable registration software, local registrations cannot be applied to correctly

align regions where patient positioning may be different between images. Identical

patient positioning however, does not eliminate the differences that may occur in two

images due to patient respiration. Goerres et al92

performed a study which examined

using different breath-hold techniques to ensure correlated anatomy on both CT and

PET images acquired on a combined CT/PET scanner. However, for non-hardware

registered CT-PET images, a study could not be found in the published literature

which evaluated the affect which inter-image differences in patient internal anatomy

due to respiration have on the accuracy of rigid registration.

The approach and the techniques used for rigid registration software to iteratively

transform and evaluate the mapping of the secondary image onto the primary image,

is similarly described by various sources81, 85, 86, 88, 89

• Transformation of the secondary image onto the primary image

. These processes are:

Meas et al88

It is recommended that the initial transformation (the starting

estimate) is reasonably close to the “capture range” for optimal

registration (i.e. the overlapping of the two images most likely to

lead to optimal registration)

states that initial transformation should align the

centres and scan axes of both images.

81.

36

• Base image re-sampling

The two images being registered do not always have the same

voxel size so both images can be re-sampled to have the same

voxel size (cubic re-sampling)86

Meas et al

. 89

• Multi-resolution image sampling

describes a method of translating the voxel positional

and dimensional information in each image into co-ordinates to

avoid this re-sampling process.

The images can be re-sampled at a lower resolution (or larger grid

size) as a means of sub-sampling the images to increase the speed

of the registration process. Sub-sampling can be used to search

for an approximate alignment of the images88, 93

Like-wise the images can then be re-sampled at increasing

resolutions (smaller grid sizes) as a means of increasing the

accuracy of the registration

.

88

• Image interpolation

.

For any given transformation the voxels of the secondary image

may not be exactly aligned with those of the primary image,

particularly if there is a rotational offset between the images.

Interpolation of voxels surrounding a given point in an image is

required to determine the corresponding image intensity values

between the images89

Interpolation is also required for multi-resolution sampling to

determine corresponding intensity values between the images

.

86

• Evaluation of transformation using the similarity measure

.

The overlapping image data for a given transformation is

evaluated using the selected similarity measure to calculate a

value.

• Search optimisation

After the initial transformation, iterative transformations with

translational and rotational step sizes relative to the image

sampling resolution are performed until no further improvements

in the similarity measure values can be found. The transformation

37

with the most optimal similarity measure value is selected to

register the images85

Consideration is given to which of the different axes of the

secondary image are translated and rotated for each iterative

transformation

.

88

.

Due to the techniques just described, regions in images of extreme high or low

intensities (local maxima or minima respectively) can induce registration errors81

.

Local maxima or minima representing different patient anatomy can be incorrectly

aligned because their overlapping image voxels can be interpreted as optimal

solutions by the similarity measure algorithm. This effect can be seen when the

alignment of the images is outside of the capture range, most notably when there is

insufficient overlap of the images.

As voxel-based registration is based solely on the image data, attributes of the

images can also affect the registration outcomes. Images where a section of the

patient is scanned are referred to as truncated images. While planning CT scans

need to include sufficient anatomy for accurate treatment field localisation and dose

volume calculations, the smallest possible range of the patient is scanned to limit

unnecessary radiation exposure. Planning CT scan protocols for lung patients

stipulate the scan length starting at the intervertebral space between C7 and T1,

finishing at the base of the diaphragm. Studholme et al86

demonstrated that missing

image data at the top and bottom slices of MRI and PET brain images affects

registration accuracy.

Image spatial resolution, particularly slice thickness is another image attribute which

may impact on registration accuracy. A recent publication by Zhang et al94 on their

study evaluating four different voxel-based image registration algorithms discusses

the possibility of image registration accuracy being related to slice thickness.

Daisne et al’s95 evaluation of multi-modality registration concluded that the

accuracy of image registration is correlated to the spatial resolution of images.

However this study was conducted using a manual registration technique based on

interactive surface segmentation. It is also suggested that image registration

38

outcomes will be more robust when based on the transmission scan36, 90, 96, 97

due to

the transmission PET scan being more similar to the planning CT.

1.2.11.4 Validation of image registration

Validation of the image registration accuracy is crucial. Two validation methods that

are supported in the literature are the use of fiducial markers and visual inspection of

the registered images. Fiducial markers can also be used to verify imaged based

registration by providing a means of quality assurance82, 98. Partial voluming effects

of the imaged markers83 has potential implications for the use of fiducial markers as

a means of validating image registration. Wong et al99

performed a study to assess

the limits of visual detection of misregistration. A high level of anatomical detail

and similarity between the two images being registered assists greatly in achieving a

satisfactory level of certainty when assessing the accuracy of alignment of the two

images. Image interpretation skills are therefore an important part of the validating

registration results even when using automated techniques. Wong et al’s study used

registered PET and MRI images with introduced translational and rotational offsets.

Despite the lower spatial and contrast resolution of the PET images, observers were

able to detect offsets as small as 2mm and 2°.

1.2.12 Contouring techniques

1.2.12.1 Contouring techniques in a treatment planning system

Contours, also called regions of interest (ROIs), can be created in the treatment

planning process using a variety of methods depending on the available software.

They can be created by manually outlining a tumour volume or critical structure on

each 2D CT slice, relying solely on the user’s image interpretation ability to

delineate ROIs. The TPS combines the contours on each 2D CT slice to generate a

3D contoured volume for each ROI.

Automated techniques can also be used to create ROI’s. One such method uses a

range of CT numbers to create a 2D or 3D contour for a selected region or volume of

interest (VOI) on the CT image (see Figure 1-14). This method of contouring is

39

dependent on the selection of the CT number threshold levels. Incorrect thresholds

will not contour the required anatomical structure surface. It is most limited when

the boundaries between structures have similar CT numbers or where there is not a

continuous surface encompassing and separating a structure from its surrounding

anatomy. Threshold based contouring techniques can thus require manual editing.

Figure 1-14 Semi-automated threshold contouring techniques (a) To determine CT number threshold levels for automatic contouring of a structure (in this case the spinal canal) a profile of CT numbers is generated. The red lines on the CT number profile indicate the range of CT numbers within the spinal canal. (b) When a range of CT numbers from 900 – 1200 is used the spinal canal is correctly contoured. (c) Incorrect contouring as there is not a continuous surface of the spinal canal.

(a)

(b) (c)

40

3D model-based contouring creates contours using 3D mesh models of organs or

structures to adapt to individual patient dimensions on a CT data set. Model-based

contouring can be used to create models of regions of interest that can be used to

provide consistency of outlining from one user and data set to another100. Model

based segmentation (MBS) software in the Pinnacle3

TPS uses CT numbers as one of

the parameters for adapting the model. It also provides rules on the maximum

distance it can change from its original shape (or organ flexibility), the permissible

CT number gradient at the edge of the organ or structure being outlined, and whether

it has a positive or negative gradient. Once the mesh has been adapted it is converted

into contours on each slice which are added together to create a 3D volume.

1.2.12.2 Reported methods of GTV contouring on PET images

Various GTV contouring methods using registered CT/PET images have been

reported in the literature. These include manual techniques relying solely on visual

inspection of the registered images101 as well as various threshold methods based on

the PET image data5, 36, 37, 70, 71, 102, 103

. Threshold methods are used for deriving PET

based-contours as there is not an abrupt jump in intensity levels at the boundary of a

region of high uptake mostly due to image resolution. Different threshold values

(using either the pixel values or SUV data within a volume of interest on the PET

image) have been described in the literature (see Table 1-2).

Table 1-2 Threshold methods for contouring a GTV using the PET image data

Author Threshold method

Mah et al37 50% of the maximum intensity within a VOI

Erdi et al36 42% of the maximum intensity within a VOI (>4cm 3

Bradley et al

volumes only)

71 40% of the maximum intensity within a volume

Black et al102and Grills et al103

[(0.307x mean SUV) + 0.5853] where mean SUV = the mean SUV within a VOI

Paulino et al5 SUV > 2.5 determines the VOI edge

The recommendation that an absolute threshold value of SUV > 2.5 be used to define

the edge of a GTV5 appears to be based on Patz et al’s104 evaluation of SUV’s as an

41

indicator of the malignancy of pulmonary abnormalities on PET images. Black et

al’s102 phantom study evaluated the effect of volume, variation in 18

F-FDG

concentration within a volume and background activity on contouring outcomes

based on a VOI mean (described in Table 1-2). It was found that a VOI mean SUV <

2 affected the accuracy of their threshold method, however volume size and

background activity were found to have no effect.

Erdi et al105 tested a method for accurate segmentation of lesions in the lung for PET

images. An adaptive threshold method was found to produce accurate volumes,

where the threshold level used varies depending on the source-to-background activity

and the size of volumes. While the purpose of this study was to determine accurate

volumes of lesions on PET images for more effective administered dosages for

radionuclide therapies, it has potential implications for threshold levels for accurate

GTV definition in radiotherapy treatment planning. Threshold levels of 10-20% of

the VOI maximum were found to accurately contour a moving sphere in a phantom

study70

. These results are significantly different from the previously mentioned

studies which were performed on patient images in which the PET images would

have been acquired under the influence of respiration. Which of the described

contouring techniques for PET-determined GTVs can provide the most efficient

approach as well as accurate volumes for radiotherapy treatment planning is

uncertain.

When considering the implementation of any contouring protocol in radiotherapy,

techniques to reduce inter-user variation are important. The use of an accurate

threshold technique may be negated by inconsistencies in the protocol which result in

high inter-user variation. Riegel et al106 found that an institutional protocol for

contouring on registered CT/PET images was required as the use of registered

CT/PET images in treatment planning without contouring protocols did not eliminate

differences between observers. While Mah et al37 observed a reduced rate of inter-

observer variability in tumour definition using CT/PET registered images, they also

concluded that more uniform GTV definition protocols for the radiation oncologists

were required. The inclusion of appropriate viewing windows as part of a CT/PET

contouring protocol was found to reduce inter-user variation107, demonstrating the

importance of the manipulation of the images within image fusion software.

42

1.3 Aims of the project

1. To perform a quality assurance study of the processes associated with using

registered CT and PET scans for tumour volume definition for radiotherapy of

lung cancer patients based on CT and PET images of a phantom. This quality

assurance study will be used to

• Investigate image acquisition and manipulation techniques for registering

and contouring CT and PET images in a radiotherapy treatment planning

system.

• Determine technology and image-based errors in the registration and

contouring processes of these images.

2. To use the outcomes of the phantom image based quality assurance study to

determine clinical protocols for:

• Acquiring patient PET image data for incorporation into the 3DCRT

process.

• CT/PET registration.

• GTV definition.

3. To test the developed clinical protocols to assess levels of accuracy and

reproducibility attributed to the use of these protocols. This will be achieved by

the participation of radiation therapists and radiation oncologists in image

registration and GTV definition trials using patient images

43

1.4 Ethical considerations

There were no ethical considerations for any stage of the research involving the use

of the phantom. Standard protocols regarding radiation safety and workplace health

and safety were adhered to when PET and CT scanning the phantom. The target

delineation and registration protocol development using the PET and CT data for the

phantom did not require the participation of any individuals other than the principal

researcher.

Acquisition and use of phantom data for determination of image registration

and target volume delineation protocols

The NHMRC guidelines

Acquisition and use of patient data, and RT and RO participation for the image

registration and target volume definition protocol trials 108

1. The use of patient PET and planning CT scans and

for research involving data collection and participants

were adhered to. Ethics approval was sought for:

2. The RT and RO participation in the image registration and the GTV

delineation trials.

Submissions were made to both the Princess Alexandra Hospital’s and the

Queensland University of Technology’s Human Ethics Research Committees

(HREC). While the research was conducted in collaboration with the Mater

Radiation Oncology Service at the Mater Hospital, this service is under the

management of the PAH. A full HREC application was made to the PAH HREC

with a Level 2 (expedited) HREC application was made to QUT’s HREC. Approval

was granted by both committees.

It needs to be noted that the patient data was not acquired with the intent to intervene

in standard practices involved in the patients’ diagnosis and treatment planning

procedures. The patients received no extra radiation exposure as no scans were

performed solely for the purpose of this research. Patients whose data might be able

to be used for the purposes of this research were identified at clinical presentation

and were followed through their routine staging and planning procedures. This was

Patient data acquisition

44

done as patient data would be used for the RT image registration and RO GTV

delineation trials if they met the following criteria:

1. Only patients that might be a candidate for radiation therapy, requiring a

routine diagnostic staging PET scan, were targeted for potential use of their

PET data.

2. Only patients who would need a routine planning CT scan performed as part

of their treatment planning procedure prior to treatment delivery.

3. Only patients who were positioned in the same position for their PET scan as

their planning CT scan.

In order to satisfy criteria 3 for patient data use for the study, some patients were

PET scanned on a flat couch top with the appropriate stabilisation devices and their

arms up. Wide consultation with referring specialists, nuclear medicine

technologists, physicists and radiologists determined that this would have no impact

on the outcome of the PET scan for diagnosis and staging purposes. The stabilisation

equipment would not produce image artifacts. Staging scans for lung cancer patients

were routinely performed with the patient’s arms up or down. Advice was sought as

to whether there were any ethical concerns regarding the patient positioning at the

PET scanning stage and no concerns were raised as there would be no deviation from

routine protocols for PET scans for lung cancer staging.

Initially it was thought that fiducial markers should be used during the patients’

routine staging PET scan to provide a means of reference for analysis of the

registration protocol trials involving the RTs. However these PET scans were used

by a number of medical specialists for determining the patients’ treatment

management. It was felt that the fiducials could very easily be mistaken for

pathology and thus have the potential to alter the patients’ clinical management due

to a false diagnosis. The risks to the patient outweighed any benefits of using the

fiducial markers for this research and it was decided not to attempt to use them for

the patient PET scans. Other means of analysing the outcomes of the RT registration

trials were sought.

45

Patient consent was not required for use of their PET and planning CT scans in the

image registration and GTV delineation trials. These scans were part of the patient’s

routine clinical staging and treatment planning procedures and were to be used

retrospectively after the patient had finished their treatment planning. It was

considered routine clinical patient data and approval was given to use the data if it

was suitably de-identified so that the data could not be connected to the individual

patient. The patients’ medical history (i.e their disease staging and the PET scan

report from their radiotherapy charts) was used to provide information to the RO’s

for the GTV delineation study. This information was also de-identified prior to the

commencement of the study as per ethics approval requirements.

Use of patient PET and CT data in trials

As the data was being used retrospectively, consideration needed to be given to

protect the professional integrity of the radiation oncologists and radiation therapists

involved in the planning process of these patients. Any information that could be

identified with a patient and their treatment process, including any health practitioner

involvement, was removed from the patient data used.

The ROs and RTs were classed as participants in the image registration and GTV

delineation trials and there were some ethical considerations that needed to be

addressed. Approval was given for these trials as any risks associated with

participation were identified and addressed where necessary.

RT and RO participation in the image registration and GTV delineation

protocol trials

An identified potential risk to the participants associated with the trials was if

individual participants could be identified with their performance. In this situation

the participant may have felt that their professional reputation could be

compromised. To avoid this, prior to commencement of the study, all participants

were assigned an identifying code that was known only to the participant and the

principal researcher.

There were concerns that the time commitment by participants might be excessive.

This was particularly an issue with the RO involvement. Initially it was thought that

46

the ROs would 1) contour the GTV on the CT alone, then 2) contour on the

registered CT/PET data without protocols and 3) use the registered CT/PET data

with contouring protocols. The procedure of the GTV delineation trial was reviewed

to keep the total time participation within what was considered reasonable. It was

decided to eliminate the second step of contouring on the registered CT/PET data

without the protocols.

Participation was voluntary. Participants were required to read the participant

information document and sign a consent form with the understanding that they

could withdraw from the trials at anytime.

1.5 Research agreement

It was a requirement that a research agreement be drawn up between the PAH and

QUT concerning the RO and RT trials. The standard research contract between the

PAH and QUT was used as the basis of this agreement.

47

2

2.1 Phantom design and construction

Image acquisition and analysis

2.1.1 Aims of phantom construction

To design a phantom that would simulate the chest region of a patient with the

effects of air in the lungs, a lesion or nodes in the mediastinum and a moving lesion

in the lungs. Specifically the phantom was required to provide CT and PET images

that could be used to:

1. Investigate the effects of the different spatial resolutions and voxel sizes of CT

and PET on imaging and contouring objects of different size. It was anticipated

that the known geometry of the phantom and the superior spatial resolution of the

CT images could be used as a template for comparing with the PET images.

2. The effects of increasing background uptake on the ability to image different

sized objects with PET accurately.

3. The effects of motion on an imaged object for both PET and CT images.

The phantom also needed to produce minimal artifacts and needed to fit through the

apertures of both the CT and PET scanners, allowing for the full dimensions of the

phantom to be scanned within the maximum FOV.

48

2.1.2 Phantom dimensions and functions

A two-part phantom was specifically designed and constructed for the purposes of

this research. It consisted of a main tank and a variable speed motor-driven moving

sphere that could be placed in the phantom as required.

2.1.2.1 Main tank design

The main tank was constructed entirely of Perspex to avoid artifacts from metal in

both the CT and PET images. Ideally the phantom would have been constructed with

no sharp edges to better simulate the shape of the chest but this would have been cost

prohibitive. Figures 2-1 and 2-2 illustrate the design of the main tank.

Figure 2-1 Main tank design: INF / Feet view A=Main tank, B=2.0 cm central rod, C=1.0 cm central rod, D=0.5 cm central rod, E=1.5 cm central rod, F=3.0 cm central rod, G and H=air cavities. (NB Drawing not to scale).

= mid-tank sagittal plane

A

B

C

D

E

F

G

40 cm

40 c

m

H

49

The functional features of the main tank are:

Region A: The main tank which could be filled with water and/or 18

F-

FDG. There were two regions of air on either side of the tank.

Its maximum dimension of 40 cm was chosen as this was a

reasonable size for an adult male’s chest and also as a result of

the 56 cm bore size on the PET scanner that was to be used for

the study.

Regions B-F: Isolated rods within the main tank which could be filled with

water and/or 18

F-FDG. This allowed different ratios of

activity per ml in these rods to that used in the main tank as

required.

Regions G and H: Region G and H were to simulate the air cavities of the lungs.

Region H was left open on one side to facilitate easy and

greater flexibility in the positioning of the moving sphere

within the main tank.

50

Figure 2-2 Main tank design: Side view at the mid-tank sagittal plane A=Main tank, B=2.0 cm central rod, C=1.0 cm central rod, D=0.5 cm central rod, E=1.5 cm central rod, F=3.0 cm central rod. (NB Drawing not to scale).

The volume in ml of regions A to F were determined (see Table 2-1) so that different

ratios of activity per ml could be calculated and injected into the main tank of the

phantom compared to the isolated central rods. This would simulate different

background to tumour uptake ratios.

A

B

C

D

E

F

SUP / H

ead

INF / Feet

200mm

51

Table 2-1 Capacity (ml) of different compartments in phantom Total ml in compartments B-F = 254 ml

Phantom Capacity

Compartment Diameter of central rods Capacity (ml)

A Main tank N/A 12500 ml

B 3.0 cm 143 ml

C 1.5 cm 34 ml

D 0.5 cm 3 ml

E 1.0 cm 15 ml

F 2.0 cm 59 ml

2.1.2.2 Moving sphere design

A hollow Perspex sphere that allowed various liquids to be injected into it for

imaging on a CT and PET scanner was required. This sphere needed to oscillate

along a fixed trajectory within the body of the existing phantom at 15 to 20 cycles

per minute. To enable this to happen, a motor and appropriate supporting structures

for the sphere were required.

The final design of the moving sphere is shown in Figure 2-3. Specific features of

the moving sphere were:

• The outer sphere diameter = 4.5 cm

• Inner sphere diameter = 3.5 cm

• Capacity of sphere = 24 ml

• A variable speed motor was used so that different respiration cycles of a

patient could be used when using the sphere to simulate a lung lesion.

• The trajectory of the sphere was straight not elliptical.

• The maximum displacement of the sphere along its path of trajectory was 2.0

cm.

• A major design specification was to ensure that the motor driving the sphere

was at a sufficient distance from the sphere so that it would always be outside

of the FOV for all the scans

52

• The mount of the sphere was specifically designed so that it could be placed

at various positions and angles within the main tank of the phantom.

Figure 2-3 Photograph of moving sphere, including mount and motor (a) = Hollow sphere mounted on rod with variable speed motor to simulate respiratory motion (b) =Demonstration of the variable angulation of the rod and hence possible variation in direction of the moving sphere relative to the scan plane (c) = Positioning of sphere and mount relative to the main tank. The arrow indicates the direction of the motion of the sphere.

(a) (b)

(c)

53

2.2 Phantom image acquisition and analysis

2.2.1 Aims

1. To acquire CT and PET images of the constructed phantom which simulate

different patient specific conditions that could be used to investigate image

registration and GTV delineation processes. This was to provide data for:

• Determining whether PET AC emission scans or, AC emission and

transmission scans were required to provide more effective image

registration outcomes.

• The effects of using free breathing CT and PET scans on image


• The effectiveness of fiducial markers as an aid in CT/PET registration.

• Determining the effect of increasing background to lesion ratios on

threshold levels for ROI geometric edge detection for GTV delineation.

• Determining the effects of motion on threshold levels for geometric edge

detection for GTV delineation.

2. To perform baseline volume measurements on the CT scans of the phantom using

the contouring tools in the treatment planning system. The baseline contours will

be converted into 3D models of the different features of the phantom that can be

loaded onto the PET AC images to:

• To evaluate the level of accuracy that may be achieved for PET-based

contours.

• To extract image data for different volumes of interest on the PET AC

scans.

3. Investigate image manipulation and quantification in a treatment planning system

to ensure accurate geometrical visual representation of the PET images as well as

the validity and appropriate use of the PET image data.

4. Investigate the effects of respiration-induced motion on an imaged volume for

both CT and PET images so that these effects can be factored into the image

registration and contouring processes that are to be investigated.

54

2.2.2 Methodology

2.2.2.1 Phantom CT scan acquisition

All of the CT scans of the phantom were performed on a Siemens Somatom “Open

Sensation” 20 slice, wide bore CT scanner.

A plastic template was made which had the position of the main tank and the moving

sphere support mount drawn on it (see Figure 2-4). This was to ensure a

reproducible and efficient set up of the two components of the phantom when the

sphere was required to be imaged with the main tank. The main tank was positioned

with the inferior/feet end of the tank facing the bore of the scanner. The lasers on the

CT scanner were aligned with the mid-tank sagittal and transverse planes drawn on

the main tank.

Phantom set up on the scanner

Figure 2-4 Photograph of phantom set up for scanning Main tank of phantom with moving lesion, both mounted on base template

55

When the moving sphere was required for a scan it was placed so that the motor and

mount were at the inferior/feet end of the phantom. The sphere’s position relative to

the main tank is shown in Figures 2-5 and 2-6.

Figure 2-5 Moving sphere scanning position in main tank: INF / Feet view A=Main tank, B=2.0 cm central rod, C=1.0 cm central rod, D=0.5 cm central rod, E=1.5 cm central rod, F=3.0 cm central rod, G and H=air cavities, I=moving sphere (drawing not to scale).

= mid-sphere sagittal plane

The black circle (Region I) as shown in Figures 2.5 and 2.6 demonstrates the position

of the sphere relative to the tank when it was static. The red and blue circles in

Figure 2-6 show the path of oscillation of the sphere and the maximum displacement

relative to the superior /inferior directions of the phantom.

A

B

C

D

E

F

G H 8.5cm

I

56

Figure 2-6 Central sphere sup/inf scanning position in main tank: side view at the mid-tank sagittal plane A=Main tank, I=position of sphere when static=central sup/inf position of oscillation (drawing not to scale).

IZI MM3003 multi-modality fiducial markers were used (see Figure 2-7). The

markers consist of a sealed gel ring that is visible on CT images. There is a cavity in

the centre of the ring for injected radioactive pharmaceuticals for nuclear medicine

imaging. Exact manufacturer specifications of the markers are:

Use of fiducial markers

• 15 mm outer diameter

• 3.5 mm thick

A

Mid transverse plane

Mid–sphere coronal plane

SUP / H

ead

INF / Feet

Centre of sphere max. superior displacement = 1cm

Centre of sphere max. inferior displacement = 1cm

5.5cm from bottom of sphere to main tank

10.0cm

Perspex rod sphere mounted on

I

57

• 5 mm axial hole (http://www.izimed.com/mmm.asp)

Figure 2-7 MM3003 multimodality fiducial markers

The multimodality fiducial markers were placed on the top of the phantom on both

the left and right sides and were aligned with the mid transverse plane (half the

length) of the phantom. The fiducial marker positions are indicated in Figure 2-8.

Figure 2-8 Position of fiducial markers on phantom

A series of CT scans of the phantom were acquired with all components static. The

planning CT scans for lung cancer patients are performed with the patient head first

into the scanner, with the table moving into the scanner during the scan, scanning the

patient in a superior to inferior direction. The specific CT scanning procedure,

including phantom set up, for this series of scans was:

Static CT scanning of the phantom

Sealed gel ring with adhesive backing

Injection site for 18-FDG

Fiducial positions on phantom

http://www.izimed.com/mmm.asp

58

• The base plate with the main tank alone with and without the sphere was

placed so that the superior/head aspect of the main tank was facing the

bore of the scanner.

• MM3003 fiducial markers were placed on the main tank in same positions

indicated in Figure 2-8.

• The base plate was positioned so that the transverse laser on the scanner

was aligned with the mid-transverse plane of the main tank. (This was

also through the centre of both fiducial markers)

• The scanner zeroed at the mid-transverse plane of the markers

• “Patient” position:

Supine

Head first

Table moves into scanner

• Scan length: This was selected to ensure a minimum of 1 cm overshoot of

the main tank at both the superior/head and inferior/feet aspects.

• 3 mm or 5 mm slice thickness

• Field of view (FOV) = 750 mm

• The standard chest imaging protocol that was used for all lung patients

was selected:

70 mAs

120 kVp

Pitch = 1.2

• Image reconstruction

512 x 512 mm matrix size

Siemens adaptive multiplanar reconstruction (AMPR) algorithm

Table 2-2 indicates the specific conditions of each of the acquired “static” CT scans

of the phantom.

59

Table 2-2 The “static” phantom CT scan acquisition parameters Scan no. Conditions of scan acquisition

1 The main tank alone with the central rods filled with water and the main tank empty. Slice thickness = 5 mm

2 The main tank alone with both the central rods and the main tank filled with water. Slice thickness = 5 mm

3 The main tank with the central rods filled with water, the main tank empty and the sphere filled with water in the static position. Slice thickness = 3 mm

A series of CT scans to represent a “free breathing” scan of a lung cancer patient

were performed on the phantom. The scanning procedure and parameters for this

series of scans was exactly the same as for the static scans of the phantom except the

sphere was scanned moving at different rates and a slice thickness of 3 mm was

selected all of the scans. Table 2-3 lists the details of each scan series.

“Free breathing” CT scans of the phantom

Table 2-3 Phantom conditions for the “free breathing” phantom CT scans Series no. Conditions of scan acquisition

Series 1 5 scans with the central rods filled with water, the main tank empty and the sphere filled with water oscillating at 15 cycles/min.

Series 2 5 scans with both the central rods and the main tank filled with water, and the sphere filled with water oscillating at 15 cycles/min.

Series 3 5 scans with the central rods filled with water, the main tank empty and the sphere filled with water oscillating at 20 cycles/min.

Series 4 5 scans with both the central rods and the main tank filled with water, and the sphere filled with water oscillating at 20 cycles/min.

A 4D CT scan was required so the 4D volume of the sphere could be measured from

CT to use as a reference for measuring the effects of motion on the threshold values

for geometric edge detection on the PET scans of the moving sphere. The CT

scanner used did not have 4D CT capabilities so a 4D scan was simulated. The

procedure and scan parameters were exactly the same as for the static CT scans of

Simulation of a 4D CT scan of the moving sphere

60

the main tank and the sphere except the sphere position was off-set from the central

position in 5 mm increments. Five CT scans of the phantom were performed with

the sphere static but in each of the different positions shown in Figure 2-9.

Figure 2-9 Scanning positions of the sphere to simulate a 4D CT volume of the moving lesion

0cm displacement of sphere 0.5cm sup displacement of sphere 1.0cm sup displacement of sphere -0.5cm inf displacement of sphere -1.0cm inf displacement of sphere

SUP / H

ead

INF / Feet

Mid transverse plane of main tank

Mid–sphere coronal plane

61

2.2.2.2 Phantom PET scan acquisition

All of the PET scans of the phantom were performed on a Philips Allegro stand-

alone PET scanner. The Allegro PET scanner utilises GSO detectors with a 137

Cs

gamma source for transmission imaging.

2.2.2.2.1 Preliminary phantom test PET scans

A series of preliminary test scans on the main tank phantom were performed to

determine a suitable activity to inject into the phantom. The SI unit for activity, the

Becquerel (Bq) was used. The aim was to find a suitable activity per ml (Bq/ml) that

would provide equivalent image quality as seen in clinical studies using the same

scanning protocol used for scanning lung patients. In particular the individual rods

needed to be well visualised. The decay of the activity of the

Determination of activity for PET scanning phantom

18

F-FDG was taken

into account for any time that may elapse between injection of activity and scan time

to ensure the Bq/ml at scan start time was known. The phantom was scanned:

1. With 30 MBq in 11000 ml of water in the main tank of the phantom (0.0027

Mbq/ml) with no water or activity in the central rods.

2. With 1 MBq in 254 ml in the central rods (0.0039 MBq/ml).

3. With 5 MBq in 254 ml in the central rods (0.0197 MBq/ml).

4. With 5 MBq in 254 ml in the central rods and 12.3 MBq in the main tank

(0.0197 MBq/ml in all compartments).

A qualitative method of assessing the correct activity/ml for the phantom was used.

The standard nuclear medicine window levels for viewing patient scans were applied

to the images and the visibility of the central rods was then assessed. Several

different concentrations of activity were trialled until all the central rods could be

clearly seen, with and without background.

Part of the GTV delineation study was to determine if SUV’s and/or a percentage of

the maximum uptake in a ROI could be used to determine thresholds for geometric

edge detection. The Pinnacle

SUV determination

3 treatment planning system is able to read the SUV’s

62

of a PET scan only if an SUV study has been performed. The Philips Allegro PET

scanner can determine SUV on AC images if the weight of the subject and the

injected activity are entered at the same time as the patient data and scan parameter

prior to initialisation of the scan. The water volume (12500 ml) of the main tank was

weighed as 13 kg. This was entered as the subject weight. The activity at the time it

was initially drawn up for injection was recorded and entered for each scan.

The phantom set-up on the PET scanner was the same as for when the CT scans of

the phantom were performed. When the sphere was scanned, the set up of the main

tank and sphere relative to each other was the same as for the CT scans using the

base plate to ensure reproducibility. MM3003 fiducial markers were placed on the

main tank in same positions as for the CT scans (see Figure 2-8). The markers were

injected with

PET scan parameters

18

• A 20 ml syringe was drawn up with a solution of saline and 2 MBq of 18-

FDG (see Figure 2-10a). This solution resulted in a concentration of

activity of 0.1 MBq/ml.

F-FDG using the following method:

• A 27 gauge needle or smaller was used to inject the saline-18

• The needle was withdrawn slowly at the angle of injection.

F-FDG

solution. The needle had to be inserted sideways through the gel into the

cavity (see Figure 2-10b). Only a few drops of the solution were required

to fill the marker cavity. Too much resulted in the solution leaking

through the injection site (see Figure 2-10c).

Figure 2-10 Fiducial marker injection technique

(a) (b) (c)

Lung cancer patients were routinely scanned with a whole body AC protocol,

positioned feet first into the scanner. The table is the moved out of the scanner as the

63

patient is scanned from head to feet. The entire length of the phantom was scanned

with the scan start position above the superior/head aspect of the phantom. The same

clinical scanning and image reconstruction protocols that would typically be used for

PET scanning lung cancer patients were used for scanning the phantom. These were

as follows:

• Patient weight: 13 kg (weight of main tank of phantom full of water)

• Patient orientation : supine feet first table direction out

• Scan length = 264 mm

• FOV = 576 mm

• 2 emission frames: scan time=3 mins/frame

• 4 transmission frames: scan time=1.54 mins/frame


144 x 144 mm image matrix size

Reconstruction algorithm = body/row-action maximisation-

likelihood algorithm (RAMLA)3D/3D AC/SUV

2.2.2.2.2 Different phantom PET scan conditions for use in image registration

and GTV delineation protocol testing

The phantom was PET scanned with different main tank Bq/ml to that in the central

rods and the sphere. The different PET scanning conditions of the phantom are

detailed in Table 2-4. All of the PET scans were performed with fiducial markers.

Scans were performed with decreasing TBRs (i.e. increasing levels of main tank

activity relative to the central rods). When the sphere was imaged it was either static

or oscillating at 20 cycles/min. Six scans of each condition were performed as this

would give different levels of emitted counts for each image, due to the rate of decay

of the initial activity for each image.

The injected concentration of 18F-FDG activity in water was maintained as 0.0197

MBq/ml in the central rods and the sphere for the different series of PET images,

except for those in series 2, where 1 MBq was injected into each rod. This resulted

in different concentrations of activity of 0.3333 MBq/ml, 0.0667 MBq/ml, 0.0294

MBq/ml, 0.0169 MBq/ml and 0.0069 MBq/ml for the 0.5 cm, 1.0 cm, 1.5 cm, 2.0 cm

and 3.0 cm central rods respectively.

64

Table 2-4 Different PET scanning conditions of the phantom for images to be used to test image registration and GTV delineation protocols The rate of decay formula was used to calculate the activity initially required to take into account elapsed time from the drawing up of activity for injection to the initialisation of the PET scan. This ensured that there would always be the same activity in the phantom at the initialisation of each scan.

*Activity increased to keep same Bq/ml due to additional 24 ml in sphere

Condition /Series Sphere Target-background ratio (TBR)

MBq / rod (+sphere where applicable)

volume (ml)

MBq / tank volume (ml)

Total scans Name of scan

1 - 0% background activity 5MBq/254 ml 0/12500 6 1a 1b 1c 1d 1e 1f

2 - 0% background activity 1MBq in each rod 0/12500 6 2a 2b 2c 2d 2e 2f

3 Static 0% background activity 5.5MBq/278 ml* 0/12500 6 3a 3b 3c 3d 3e 3f

4 Moving 0% background activity 5.5MBq/278 ml* 0/12500 6 4b 4c 4d 4f 4g 4h

5 - TBR = 20 (main tank = 5% activity of rods) 5MBq/254 ml 12.3/12500 6 5a 5b 5c 5d 5e 5f

6 Moving TBR = 10 (main tank = 10% activity of rods) 5.5MBq/278ml* 24.6/12500 6 6a 6b 6c 6d 6e 6f

7 Moving TBR = 5 (main tank = 20% activity of rods) 5.5MBq/278 ml* 49.2/12500 6 7a 7b 7c 7d 7e 7f

8 Moving TBR = 2.5 (main tank = 40% activity of rods) 5.5MBq/278 ml* 98.4/12500 6 8a 8b 8c 8d 8e 8f

65

2.2.2.3 Image quantification using the treatment planning system tools

The images of the phantom were exported from the CT and PET scanners in Digital

Imaging and Communication in Medicine (DICOM) format and burnt onto CD

before being imported into the Philips Pinnacle3

treatment planning system. Both the

PET AC and transmission scans, were imported.

The inner volumes of the rods and the inner and outer volumes of the static sphere

were calculated based on the known geometry of these objects in the phantom.

Baseline 3D contours were then generated in the TPS for the CT scans of the

phantom using the scan 3 of the phantom from the “static” CT scan series (see Table

2-2). The visible inner dimensions of the rods and the inner and outer dimensions of

the static sphere were contoured on the axial slices of the image using a semi-

automated thresholding technique to detect either the air/perspex or water/perspex

interfaces (see Fig 2-11). A volumetric 3D contour was automatically generated by

the TPS software based on each axial slice’s contour for a region of interest.

Baseline contouring of the CT images of the phantom

Models of the baseline contours of the central rods and the sphere were created for

extracting image pixel and SUV data from the PET images of the phantom. These

were created by converting the baseline 3D contours into 3D models using the MBS

tools in Pinnacle3

• 3D threshold-generated contours of each of the separate central rods.

. These models were then loaded and positioned on the CT image

and converted back into a 3D contour to assess the level of accuracy of 3D model

generated contours. Using the TPS ROI statistics tools, the following volumes were

computed:

• 3D threshold-generated contours of the inner and outer dimensions of the

sphere.

• 3D model-generated contour of each of the separate central rods.

• 3D model-generated contour of the inner and outer dimensions of the

sphere.

66

Figure 2-11 Contouring of the central rods on scan 3 from the “static” CT scan series of the phantom Image (a) demonstrates the threshold-based contouring for both the central rods and the static sphere. Image (b) demonstrates the threshold-based contouring for both the outer and inner dimensions of the static sphere.

(a)

(b)

Volumes of the moving sphere (inner and outer dimensions) were calculated based

on its known geometry and range of motion. To visually assess the impact of motion

on both the free breathing CT and PET AC scans in the absence of 4D scanning

technology, the TPS contouring tools were used to generate a contour representing

the 4D volume of the moving sphere. These were created by:

Creation of a 4D model of the moving sphere

• Registering the CT scans with the sphere scanned statically in the 5

different positions encompassing its full range of motion.

• Placing the previously determined 3D model of the inner dimension of the

static sphere on the imaged position of the sphere on each of 5 scans and

combining them to produce a 3D contour that encompassed the sphere’s

range of motion (Figures 2-12a to 2-12e).

• The contour of the 4D volume was then converted into a 3D model using

the MBS tools.

67

• This method was repeated to obtain a 4D volume of the outer dimensions

of the sphere (Figure 2-13f).

• The volume of each of the contours and models of the 4D volume of the

moving lesion were calculated using the TPS tools.

Figure 2-12 Creation of the 4D models of the moving sphere The 3D model of the inner dimensions of the static sphere has been placed on the, (a) Static scan with the sphere displaced 10mm sup (red contour), (b) Static scan with the sphere displaced 5mm sup (green contour), (c) Static scan with the sphere displaced 0mm (blue contour), (d) Static scan with the sphere displaced 5mm inf (purple contour), (e) Static scan with the sphere displaced 10mm inf (orange contour), (f) The 3D model of the outer dimensions of the static sphere has been placed each of the static scans (the scan with the sphere displaced 0mm is shown).

(a) (b) (c)

(d) (e) (f)

68

Each of the separate 3D models of the rods was loaded onto each AC emission scan

in each of the different series of the PET phantom images. Each model was

converted into a 3D contour (see Figure 2-13) and their volumes were computed

using the TPS ROI tools. These values were compared with the calculated and the

3D model generated volumes on the CT images of the phantom.

Contouring of the PET AC images of the phantom

Figure 2-13 3D models positioned and converted to contours on the PET AC images of the phantom (a) Transverse view of the phantom with the 3D models converted to contours for the central rods. The light-green circle is the 3D spherical contour used to determine the pixel and SUV data in the main tank. (b) Sagittal view of the phantom demonstrating the 3D models converted to contours for the central rods.

(a) (b)

The CT/dose tool in the Pinnacle

Quantification and validation of PET pixel and SUV data in the TPS 3 TPS was used to determine the PET AC image

data that could be obtained in the planning system (see Figure 2-14). There were two

values for each pixel of the PET AC emission images; the first was a value with no

indicated units and the second was clearly the SUV data. The expected density value

of the water and 18F-FDG solution was 1.0 g/cm3, not the 0.84 g/cm3

given by the

TPS for the pixel selected in the middle of the 2 cm rod.

69

Figure 2-14 PET AC image pixel data available using the CT/dose tool The cursor was clicked in the middle of the 2 cm rod (as indicated by the yellow arrow) which was filled with the water and 18

F-FDG solution. Two values for the selected pixel within the 2 cm rod were given, an actual pixel value of 8210 and an SUV of 38.87.

The ROI statistic tools were used to obtain the minimum, maximum and mean pixel

and SUV value within each 3D contour of the rods and the sphere (where imaged) on

the AC emission images. Background pixel and SUV values for series 5 – 8 were

obtained by positioning a 3D contour of a sphere in the main tank of the phantom

(the light-green contour in the main tank of the phantom in Figure 2-13a).

2.2.2.4 Determination of window width and level for PET images

Scan 2 from the “static” CT scan series was registered with each of the images (both

AC and transmission) from the PET scan series 1, 2 and 5 of the phantom in the

Pinnacle3

TPS. Static CT Scan 4 was registered with the images from the PET scan

series 3, 4 and 6 to 8.

The window widths and levels were adjusted on each of the PET images:

• On the AC scans until:

70

1. The regions of uptake in the rods and the main tank visually

matched the inside dimensions of the rods and main tank on the

CT image (the inside dimensions of these features correspond to

the injected 18

2. The regions of uptake in the central rods visually matched the

inside dimensions of the rods in the CT scan. This resulted in the

background being windowed out for the PET AC images from

series 5 -8). This is demonstrated in Figure 2-15b.

F-FDG fluid volume on the PET AC images). This

is demonstrated in Figure 2-15a.

• On the transmission scans (see Figure 2-15c) until

3. The outer dimensions of the main tank matched those on the CT

scan

The window width and levels of the PET scans that resulted in the required

visualisation of the different features of the phantom were recorded for both the AC

emission and transmission scans for each series of the PET scans.

To determine the window widths and levels for visualising the static or moving

sphere on the PET AC scans in series 3, 4 and 6 to 8, the model of either the static or

4D volume of the sphere was loaded onto the CT image over the known coordinates

of the sphere’s centre. The window width and level of the PET image was adjusted

so that the dimensions of the sphere on the PET image visually matched the model of

either the static or 4D volume of the sphere (see Figure 2-16).

71

Figure 2-15 Visualisation of the methods used for determining appropriate window width and levels for viewing PET images Images (a) – (c) show transverse, sagittal and coronal views of the phantom with the registered images of CT static scan 2 and PET series 5 (a) Demonstrates the registered PET AC and CT scans displayed as a blending of the two images. The PET AC scan has been windowed so that the background and rods are visible, with the dimensions of the high uptake of the central rods matching the inner dimension of the rods on the CT image. (b) Demonstrates the registered PET AC and CT scans with the CT image displayed partly with a cut-out showing only the PET AC image along one half of the central rods. The PET AC image has been windowed so that only the rods are visible, again with the dimensions of the high uptake of the central rods matching the inner dimension of the rods on the CT image. (c) Demonstrates the registered PET transmission images and CT scans with the CT image displayed partly with a cut-out showing only the PET transmission image along the inferior half of the main tank of the phantom. The PET transmission scan has been windowed so that the outer dimensions of the main tank on the phantom match those on the CT scan.

(a) (b) (c)

72

Figure 2-16 Determining appropriate viewing windows for the moving sphere on the PET images using the 4D model as a template The PET AC image for one of the scans in Series 4 windowed so that the outer edges of the moving sphere on the PET image visually matches the 4D model. (a) is the transverse view of the phantom through the centre of the moving sphere, with (b) is the sagittal view and (c) the coronal view. The 4D model was loaded onto the image to match the known position of the sphere relative to the main tank of the phantom.

(a) (b) (c)

2.2.2.5 Evaluation of the moving sphere on the CT and PET images

To compare the imaged moving sphere on the free-breathing CT scans with its

known range of motion and 4D volume:

• The imaged moving sphere on each of the CT scans in each of the “free

breathing” CT series of the phantom were contoured.

• The outer dimensions of the sphere only were contoured on each scan.

• The volumes of each of these 3D contours were then computed to

compare with the volume of the calculated 4D volume of the outer

dimensions of the moving sphere.

To compare the imaged moving sphere on the PET AC images (each scan in series 4,

6, 7 and 8) with its known range of motion and 4D volume:

• The window widths and levels previously determined for appropriately

viewing the sphere were applied to each image.

• Each of the images was viewed with the 4D model contour displayed so

that a visual comparison of the imaged sphere and the model could be

made.

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2.2.3 Data analysis

To evaluate the baseline TPS contoured volumes on the CT scans of the phantom:

Comparison of the baseline CT contours and PET AC contours of the phantom

• The percentage differences between the calculated volumes and the

volumes of the 3D contours and the 3D model generated contours were

determined for

The inner dimension of each central rod

The inner and outer dimensions of the static sphere

The inner and outer dimensions of the 4D volume of the moving

sphere

To evaluate the contouring results on the PET AC scans of the phantom using the 3D

models created from contours on the CT images of the phantom:

• The mean volume of the contours created by loading the 3D models of the

different components of the phantom were calculated for




sphere

• The percentage differences between the calculated volumes and the

means volumes of the 3D model generated contours were then determined

for




sphere

Using the data from the 3D model generated contours on each PET scan, ratios of the

maximum pixel value to the maximum pixel value in the 3.0 cm rod was calculated

for each rod and the sphere. The average ratio for each rod and the sphere were

calculated for each series of PET scans. This was repeated using the maximum SUV

values extracted from the contours.

Evaluation of the pixel and SUV data obtained from the TPS

74

Line plots were then created to evaluate the pixel and SUV data obtained from the

contours created on each AC image for the different PET scan series of the phantom.

Plots of the following image data were made:

• The maximum pixel value for the central rods and static or moving sphere

for each of the 6 scans in each series (a) – (f) (refer to Table 2-4)

• The mean pixel value for the central rods and static or moving sphere for

each scan (a) – (f)

• The maximum SUV value for the central rods and static or moving sphere

for each scan (a) – (f)

• The mean SUV value for the central rods and static or moving sphere for

each scan (a) – (f)

To validate the image data in the TPS, ratios of the pixel and SUV values of the main

tank to those of each different phantom component from series 5 -8 were determined.

This provided a background percentage value (or background-activity ratio) for each

of the rods and the sphere for each image. A mean background percentage was

determined for each of the phantom components for the different series. Line plots

of the series mean background–activity ratios based on the maximum and mean

values for both the pixel and SUV data were compared a plot of the known injected

percentage background activity in the main tank.

Frequency plots were made of the window width and level values that were

determined as those that best showed the different features of the PET images

according to the chosen criteria. Five different plots were made to demonstrate the

windowing levels required for the different series of PET scans to obtain an accurate

visual representation of:

Frequency plots of the window widths and level

• The main tank on the transmission scans.

• The static or moving sphere on the transmission scans.

• The background and the central rods on the AC scans.

• The central rods only with the background windowed out on the AC

scans.

• The static or moving sphere on the AC scans.

75

To evaluate whether the imaged moving sphere on the CT scans matches its known

4D volume:

Evaluation of the moving sphere on the CT and PET images of the phantom

• The mean volume was computed from contours of the imaged moving

spheres for each free breathing series of scans.

• The distance from the known central position of the moving sphere to the

most superior and inferior aspects of the imaged moving sphere were

measured on each scan. From these measurements the following was

determined:

The total imaged length of the moving sphere in the superior to

inferior direction.

The midpoint of the imaged sphere in the superior to inferior

direction.

• The mean distance of the most superior aspect of the imaged moving

sphere was calculated.

• The mean distance of the most inferior aspect of the imaged moving

sphere was calculated.

• The mean distance of the imaged sphere’s midpoint from its known

central position was calculated.

76

2.2.4 Results

2.2.4.1 Phantom CT scan acquisition

The static series of CT scans of the phantom were acquired and loaded into the TPS

system. Transverse, sagittal and coronal views of each of the scans in this series are

shown in Figure 2-17.

Static CT scanning of the phantom

Figure 2-17 Images of the series of static CT scans of the phantom (a) = Static CT scan 1, (b) = Static CT scan 2, (c) = Static CT scan 3

(a) – main tank empty with no sphere

(b) – main tank empty with static sphere

(c) – main tank full with no sphere

77

The series of “free-breathing” CT scans of the phantom were acquired and loaded

into the TPS system. Transverse, sagittal and coronal views of each of the scans in

series 1 and 2 are shown in Figure 2-18 and series 3 and 4 in Figure 2-19.

“Free breathing” CT scans of the phantom

Figure 2-18 Images of series 1 and 2 of the “free-breathing” CT scans of the phantom Transverse, sagittal and coronal views of each scan in series 1 and 2 are shown from left to right. (a) - (e) are scans a-e for series 1 (main tank empty, sphere moving at 15 cycles/min). (f) - (j) are scans a-e for series 2 (main tank full, sphere moving at 15 cycles/min).

(a) (f)

(b) (g)

(c) (h)

(d) (i)

(e) (j)

78

Figure 2-19 Images of series 3 and 4 of the “free-breathing” CT scans of the phantom Transverse, sagittal and coronal views of each scan in series 3 and 4 are shown from left to right. (a) - (e) are scans a-e for series 3 (main tank empty, sphere moving at 20 cycles/min). (f) - (j) are scans a-e for series 4 (main tank full, sphere moving at 20 cycles/min).

(a) (f)

(b) (g)

(c) (h)

(d) (i)

(e) (j)

On visual inspection all of the “free-breathing” scans demonstrate that the moving

sphere has been imaged differently, particularly in the sagittal and coronal views of

the sphere in Figures 2.20 and 2.21. These image planes visualise the superior and

inferior orientation of the phantom, which also corresponds to both the direction of

motion of the sphere and the couch movement through the CT scanner. The

transverse views are of the CT slice corresponding to the most superior imaged

aspect of the moving sphere. The shape and dimensions of the sphere on these slices

79

is varied, particularly the spiral shapes seen in some of the images. It is expected

that the sphere in these slices would be circular and the same dimensions if the

sphere had been imaged completely along its path of trajectory.

2.2.4.2 Phantom PET scan acquisition

2.2.4.2.1 Preliminary phantom test PET scans

The first test PET scan of the phantom used a solution of 30 MBq of 18

F-FDG in

11000 ml of water (0.0027 MBq/ml) in the main tank of the phantom with no activity

in the central rods (see Figure 2-20). The second test scan with 1 MBq in 254 ml of

water in central rods, a similar activity concentration (0.0039 MBq/ml) to the first

scan, failed to image the 0.5 cm and 1.0 cm central rods, indicating that this level of

activity per ml was too low for imaging the different components of the phantom.

Figure 2-20 The first test PET scan 30 MBq in 11000 ml of water in the main tank of the phantom (0.0027 Mbq/ml) with no water or activity in the central rods.

(a) (b)

The last two test scans used a concentration of 0.0197 MBq/ml in the central rods,

which equates to 5 MBq in 254 ml of water. One of these scans had this activity in

the central rods only (Figures 2-21a and 2-21b) and the other with 5 MBq in 254 ml

of water in the central rods and 12.3 MBq in 12500 ml of water in the main tank,

resulting in the main tank activity being 5% of the activity in the central rods

80

(Figures 2-21c and 2-21d). All of the central rods were clearly imaged in both of

these scans.

Figure 2-21 Test PET scans with 0.0197 MBq/ml concentration of 18-FDG Each of the images has a concentration of 0.0197 MBq/ml of a water-18-FDG solution in the central rods. Images (a) and (b) are transverse and sagittal views of the scan with only the rods filled and images (c) and (d) are transverse views of the scan with the main tank activity being 5% of the activity in the central rods.

(a) (b) (c) (d)

The solution of 20 ml of saline and 2 MBq of 18

F-FDG (0.1 MBq/ml) used to inject

the MM3003 fiducial markers was sufficient for the markers to be clearly imaged on

the PET AC images of the phantom (see Figure 2-22)

Figure 2-22 Appearance of the fiducial markers on the PET AC images

81

2.2.4.2.2 Different phantom PET scan conditions for use in image registration

and GTV delineation protocol testing

PET scans were acquired of the phantom to simulate different “patient specific”

parameters relating to injected activity, background uptake and motion of a lesion

due to respiration, resulting in 8 different series of images. Figures 2-23 and 2-24

show AC and transmission scan images from scan (a) of each of these series. 6 scans

(a-f) were acquired for each of the different series, however due to reconstruction

errors only 5 scans were acquired for series 8. The images for series 8 all

demonstrate a slight warping on the sagittal and coronal planes. A reconstruction

error also occurred during the acquisition of the scans for series 4. The left fiducial

marker in the scans from series 2 was not seen on any of the AC images for this

series (see Figure 2-23). Neither the left nor right fiducial markers were imaged on

any of the AC images from the scans for series 6 (see Figure 2-23).

82

Figure 2-23 AC emission scan images from the different phantom PET scan series

Trans AC Sag central rods AC Sag sphere AC

Series 1

Series 2

Series 3

Series 4

Series 5

Series 6

Series 7

Series 8

83

Figure 2-24 Transmission scan images from the different phantom PET scan series

Trans AC Sag central rods AC Sag sphere AC

Series 1

Series 2

Series 3

Series 4

Series 5

Series 6

Series 7

Series 8

84

2.2.4.3 Image quantification using the treatment planning system tools

There were differences between the volumes of the calculated, the 3D threshold-

based contours and the 3D model-generated contours for the central rods and the

static lesion (see Table 2-5).

Baseline contouring of the CT images of the phantom

Table 2-5 Volumes of the components of the phantom on CT – calculated and TPS generated volumes using different contouring methods

Component dimension

Calculated

volume

(cm3

3D threshold-based

contour

)

3D model generated

contour

Volume

(cm3

% diff to

calculations )

Volume

(cm3

% diff to

calculations )

Central rods 0.5 cm 2.3 2.0 -13 4.7 +104.3

1.0 cm 11.6 12.8 +10.3 15.7 +35.3

1.5 cm 32.3 31.9 -1.2 33.8 +4.6

2.0 cm 51.9 50.7 -2.3 50.1 -3.5

3.0 cm 120.9 122.9 +1.7 131.7 +8.9

Sphere Static – inside 20.6 20.3 -1.5 20.3 -1.5

Static – outside 47.7 47.1 -2.3 47.1 -1.3

The magnitude of difference between the calculated and the TPS generated volumes

on the CT image is considerably higher for the two features of the phantom with the

smallest diameters, the 0.5 cm and 1.0 cm central rods. The magnitude of the

percentage difference between the calculated and the 3D threshold-based contour

volumes for the features of the phantom with a diameter >1 cm are similar, with their

mean percentage difference equal to 1.6 ± 0.4%. The magnitude of the percentage

difference between the calculated volumes and the volumes of the 3D model

generated contours for the features of the phantom with a diameter >1 cm was more

varied, with their mean percentage difference equal to 4.0 ± 3.1%.

85

A 4D volume to provide a visual representation of the moving sphere along its path

of trajectory was generated via two different contouring techniques. There were

differences between the calculated and TPS generated volumes for the 4D volume of

the moving sphere (see Table 2-6). There was no difference in the volumes of the

4D moving sphere generated by either adding the contours of the 5 differently

positioned static scans of the sphere together or the contour generated using a 3D

model. The percentage difference between the calculated and both of the TPS

contouring methods for the inner and outer dimensions of the 4D moving sphere

were 4.4% and 3.9% respectively.

Creation of a model of the 4D moving sphere

Table 2-6 Volume of the 4D moving sphere on CT – calculated and TPS generated volumes using different contouring methods

Dimension

Volume (cm3

Calculated

)

3D threshold-based contour

3D model generated contours

4D model – inside 38.8 40.5 40.5 4D model – outside 79.6 82.7 82.7

Contours of the central rods, the static sphere and the 4D volume of the moving

sphere were generated using the 3D models of these features for each of the AC

images in series 1-8 of the PET scans of the phantom. The mean volume of each

individual contoured compartment (derived from the volumes calculated by the TPS

ROI tools) generated on the images within each series are shown in Table 2-7. The

mean volume for each of the contoured rods and spheres was derived from the

contours generated on every PET AC image is also shown.

Contouring of the PET AC images of the phantom

86

Table 2-7 Intra-series mean volumes and the overall mean volumes of the 3D model-generated contours of the phantom components on the PET AC images

Component dimension

Mean volume (cm3

PET scan series

) of the 3D model-generated contours Overall Mean ±

SD 1 2 3 4 5 6 7 8

Central rods

0.5 cm 7.3 7.7 7.2 6.0 8.5 5.8 7.5 7.8 7.2 ± 0.9

1.0 cm 16.6 21.9 16.5 22.5 14.4 21.8 16.5 16.1 18.3 ± 3.2

1.5 cm 34.5 40.9 34.8 40.2 35.6 42.4 37.4 34.6 37.6 ± 3.2

2.0 cm 61.4 62.9 61 62.7 61.7 62.2 61.4 62 61.9 ± 0.7

3.0 cm 133.6 128.6 136.2 129.7 138.3 136.9 138.8 133.5 134.5 ± 3.8

Sphere Static inside - - 21 - - - - - 21.0

Static outside - - - 45.5 - 45.1 45.3 45.4 45.3 ± 0.2

The overall mean volumes of the contours generated from the 3D models of the

central rods and sphere from all of the PET AC images are different to the calculated

volumes. The percentage differences of the calculated and the average 3D model

generated volumes for each component of the phantom for both CT and the PET AC

images are shown in Table 2-8. It can be seen that the 0.5 cm and 1.0 cm central rod

volumes for the 3D model generated contours are significantly higher than the

calculated volumes for both the CT and PET images. For the PET images, the

magnitude of the percentage difference between the calculated volumes and the 3D

model generated volumes of the central rods with a diameter >1 cm are similar, with

their mean percentage difference equal to 15.6 ± 4.1%. The percentage differences

for the volumes of the static and moving sphere are similar on both CT and PET.

Table 2-8 Percentage difference of the 3D model-generated volumes for both the CT and PET images to the calculated volumes of the phantom components

Component dimension Calculated

volume (cm3

3D model volume (cm

)

3

CT

) after conversion to contours

PET

Volume (cm3

% diff to calculations )

Mean volume (cm3

% diff to calculations )

Central rods

0.5 cm 2.3 4.7 104.3 7.2 214.1 1.0 cm 11.6 15.7 35.3 18.3 57.7 1.5 cm 32.3 33.8 4.6 37.6 16.3 2.0 cm 51.9 50.1 3.5 61.9 19.3 3.0 cm 120.9 131.7 8.9 134.5 11.2

Sphere Static – inside 20.6 20.3 1.5 21.0 1.9 4D model – inside 38.8 40.5 4.4 45.3 5.0

87

The average ratios for each PET series of the phantom of the maximum pixel value

in each rod or the sphere to the maximum pixel value of the 3.0 cm rod were not

equal to 1.0 (see Table 2-9). It should be noted that except for series 2, the same

concentration of activity was injected into each feature of the phantom (1MBq was

injected into each rod for series 2). The ratios calculated using the maximum SUV

values were identical to those calculated using the maximum pixel values.

Quantification and validation of PET pixel and SUV data in the TPS

Table 2-9 Average ratios of the maximum pixel value in each rod or the sphere to the 3.0 cm rod maximum pixel value

Component dimension Ratio PET scan series

1 2 3 4 5 6 7 8

Central rods

0.5 cm 0.21 7.13 0.23 0.2 0.16 0.16 0.16 0.23

1.0 cm 0.66 5.53 0.63 0.65 0.48 0.41 0.37 0.35

1.5 cm 0.97 4.4 1.02 0.90 0.70 0.68 0.62 0.56

2.0 cm 0.88 2.18 0.93 0.94 0.89 0.92 0.81 0.74

3.0 cm 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Sphere Static - - 0.64 - - - -

Moving - - - 0.69 - 0.61 0.56 0.49

Plots (see Appendix 1) of the maximum and mean pixel and SUV values for the

central rods and the sphere (static or moving) obtained from the TPS contours

demonstrated:

• Decay characteristics of radioactive substances when the order of the

scans acquired for each series is taken into account. This observation is

based on the plots of the pixel values.

• SUV values are a normalised value relative to total injected activity and

weight. This observation is based on the trendlines for each feature of the

phantom for:

Series 1 – 4 following the same trend as seen with the pixel value

plots (there was no background activity for these images)

Series 5 – 8 having a gradient = 0 and the SUV values

approximately halving as the background activity doubles.

88

• The pixel and SUV values in the smaller diameter rods decreased below

that of main tank as the percentage of activity in the main tank increased,

despite the central rods always having a higher concentration per ml than

the main tank.

• The range of the maximum pixel values of the different rods were similar

for

Series 1, 3 and 4 (no water or activity in the main tank), and scans

a b, and f only for series 4. The values for scans c, d, and e are

considerably higher than those observed for the other three scans

in series 4.

Series 5 – 8 (water and activity in the main tank).

• There were differences in the relationship between the plots of the

individual compartments of the phantom for maximum and mean pixel

and SUV data.

Line plots of the background-activity ratios of the main tank to the central rod or

sphere of the PET AC image data for series 5 - 8, demonstrated that only the 3 cm

diameter rod followed the known background percentage plot line for only the

maximum pixel and SUV data (see Figures 2-25a and 2-25b). The moving sphere

background-activity ratios are slightly higher than those of the 1.5 cm rod. This

agrees with the trends displayed in the plots of the maximum pixel and SUV values.

The plots of the background-activity ratio values for the mean pixel and SUV data

demonstrate a larger background-activity ratio than the known concentration of

activity in the water in the main tank compared to that in the central rods (note the 3

cm rod) and the moving sphere (see Figures 2-25c and 2-25d)

89

Figure 2-25 Plots of the background-activity ratios of the main tank pixel data to the sphere and central rod pixel data for the PET AC images: Series 5-8 with conditions as per Table 2-4. (a)=Ratios based on maximum pixel values, (b)=Ratios based on mean pixel values, (c)= Ratios based on maximum SUV values, (d)= Ratios based on mean SUV values.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Series 5 Series 6 Series 7 Series 8Series

Bkg

max

pix

el n

umbe

r/ V

OI m

ax

pixe

l num

ber

0.0

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1.0

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Series 5 Series 6 Series 7 Series 8Series

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el n

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el

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(a) (b)

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4.0

Series 5 Series 6 Series 7 Series 8

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V nu

mbe

r/ VO

I max

SU

V nu

mbe

r

0.0

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Series 5 Series 6 Series 7 Series 8

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mea

n SU

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OI m

ean

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num

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(c) (d)

90

2.2.4.4 Determination of window widths and levels for PET images

Optimal viewing window widths and levels for the PET were determined where a

geometrical visual match of different features of the phantom on both the PET AC

and transmission images and its registered CT scan was achieved. Frequency plots

(see Figures 2-26 and 2-27) demonstrated:

• The same window width (WW) and window level (WL) could be applied

to each transmission scan from all the different PET series.

• Varying viewing windows were required for the AC images and these

were dependant on the features being specifically viewed and the

percentage of activity concentration in the main tank relative to the

central rods (i.e. the background percentage).

For background and the central rod visibility a WW = 0.2 and WL

= 0 could be applied for all the images except for the series 8

images.

To view the central rods only, the same WW used for viewing

both the background and the rods with a WL of 0.5 or 0.1 was

found to provide a geometrical visual match of the inner

dimensions of the central rods on the CT images.

To view the full range of motion of the moving sphere, a WW of

0.1 or 0.05 and a WL = 0 was required.

Figure 2-26 Frequency plots of window widths and levels for viewing different features on the PET transmission scans of the phantom The transmission scans were windowed to visually match the geometry of the main tank of the phantom on the registered CT images with PET images (series 1-8)

Phantom PET trans scan window width and levels Series distribution for central rod and background visibility

0

1

2

3

4

5

6

7

8

WW 0.05WL 0.0

WW 0.05WL 0.1

WW 0.1WL 0.0

WW 0.1WL 0.05

WW 0.2WL 0.0

WW 0.2WL 0.05

WW 0.2WL 0.1

WW 0.4WL 0.2

WWL value ranges

Num

ber o

f ser

ies

with

val

ue

rang

es

Series 8

Series 7

Series 6

Series 5

Series 4

Series 3

Series 2

Series 1

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Figure 2-27 Frequency plots of window widths and levels for viewing different features on the PET AC scans of the phantom (a) = Windowing parameters for the PET AC scans (from series 1-8) where the background and central rods visually matched the geometry of these features on the registered CT images. (b) = Windowing parameters for the PET AC scans (from series 1-8) where the central rods visually matched the geometry of these features on the registered CT images). (c) = Windowing parameters for the PET AC scans to visually match the geometry of the sphere on either the registered CT images (static sphere-series 3) or the 4D model (moving sphere-series 4 and 6-8)

Phantom PET AC scan window width and levels Series distribution for central rod and background visibility

0

1

2

3

4

5

6

7

8

WW 0.5WL 0.0

WW 0.1WL 0.0

WW 0.15WL 0.0

WW 0.2WL 0.0

WW 0.2WL 0.5

WW 0.2WL 0.1

WW 0.3WL 0.0

WW 0.3WL 0.1

WWL value ranges

Num

ber o

f ser

ies

with

val

ue

rang

es

Series 8

Series 7

Series 6

Series 5

Series 4

Series 3

Series 2

Series 1

(a)

Phantom PET AC scan window width and levels Series distribution for central rods only visibility

0

1

2

3

4

5

6

7

8

WW 0.5WL 0.0

WW 0.1WL 0.0

WW 0.15WL 0.0

WW 0.2WL 0.0

WW 0.2WL 0.5

WW 0.2WL 0.1

WW 0.3WL 0.0

WW 0.3WL 0.1

WWL value ranges

Num

ber o

f ser

ies

with

val

ue

rang

es

Series 8

Series 7

Series 6

Series 5

Series 4

Series 3

Series 2

Series 1

(b)

Phantom PET AC scan window width and levels Series distribution for spherical lesion visibly matching models

0

1

2

3

4

5

6

7

8

WW 0.5WL 0.0

WW 0.1WL 0.0

WW 0.15WL 0.0

WW 0.2WL 0.0

WW 0.2WL 0.5

WW 0.2WL 0.1

WW 0.3WL 0.0

WW 0.3WL 0.1

WWL value ranges

Num

ber o

f ser

ies

with

val

ue

rang

es

Series 8

Series 7

Series 6

Series 4

Series 3

(c)

% activity concentration of the main tank relative to the central rods Series 8 = 40% Series 7 = 20% Series 6 = 10% Series 5 = 5% Series 1-4 = 0%

The sphere was surrounded by air for every scan resulting in a 0% background activity.

92

2.2.4.5 Evaluation of the imaged moving sphere on the CT and PET images

The contoured outer dimensions of the imaged moving sphere on the free-breathing

CT scans of the phantom used for comparison with the sphere’s known range of

motion and 4D volumes are shown in Figure 2-28.

Figure 2-28 Overlaid 3D contours of the moving sphere imaged for each scan from the different free breathing series of CT scans Transverse, sagittal and coronal images (from left to right) of the moving sphere are shown for each scan.

Main tank empty, sphere moving at 15 cycles/min

Main tank full, sphere moving at 15 cycles/min

Main tank empty, sphere moving at 20 cycles/min

Main tank full, sphere moving at 20 cycles/min

93

The volumes of each of these 3D contours are shown in Table 2-10. None of the

contours of the imaged moving sphere match the calculated 4D volume of the sphere

(outer dimension) of 79.6 cm3. The volumes range from 20.9 cm3 to 61 cm3 with a

mean of 40.4 ± 15.5 cm3

.

Table 2-10 Imaged volumes of the moving spherical lesion from the different free breathing series of CT scans

Series Volumes (cm3

Scan 1

) contoured to the outside dimension of the spherical lesion

Scan 2 Scan 3 Scan 4 Scan 5 Series Mean

1

(15 cycles/min Main tank empty)

60.0 37.3 45.5 21.8 23.3 37.6

2 (20 cycles/min

Main tank empty) 23.9 52.8 21.6 45.2 22.3 33.2

3 (15 cycles/min Main tank full)

42.3 59.4 30.2 25.7 61.0 43.7

4 (20 cycles/min Main tank full)

59 32.5 25.7 59.3 20.9 39.5

Figure 2-29 demonstrates the differences in the imaged superior and inferior aspects

of the moving sphere for each of the free breathing CT scans. The vertical lines of

the graph demonstrate the measured imaged length of the moving sphere in the

superior and inferior directions for each scan, from the central position of the sphere

along its plane of motion. The maximum distance between the most superior and

inferior aspects of the sphere was 65 mm along its path of motion (in the superior to

inferior directions). None of the CT images imaged the sphere’s full range of

motion. The mean centre of the imaged sphere (½ the imaged length) derived using

the data from all 20 free breathing CT scans, was found to be 2 mm inferior of the

true centre of the moving sphere. The mean imaged superior extent of the moving

sphere was 18.5 mm and the mean imaged inferior extent was 21 mm.

94

Figure 2-29 Graphical representation of the variation in the imaged superior and inferior aspect of the moving sphere from the free breathing CT scans Actual superior and inferior extents of moving sphere Mean centre of the imaged sphere (1/2 imaged length) Mean imaged superior extent of the sphere Mean imaged inferior extent of the sphere

-35

-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

35

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Imag

ed le

ngth

of m

ovin

g sp

here

(mm

)

Series 2 Series 3 Series 4Series 1

Supe

rior d

irect

ion

Infe

rior d

irect

ion

Images used for the visual comparisons of the imaged moving sphere on the PET AC

scans of the phantom with the sphere’s known range of motion and 4D volume are

shown in Figures 2-30 and 2-31. The moving sphere is shown to match the 4D

model (orange contours) for series 4 and 6, though scans (c) and (d) from series 4

appear to have slightly different dimensions and shape to the other images in this

series. However the images of the moving sphere for series 7 and 8 do not

completely match the contours of the 4D model. The sagittal images for series 8

show the moving sphere to be angled differently from its plane of motion.

.

95

Figure 2-30 Visual comparison of moving sphere imaged on PET AC scans with the 4D model: Series 4 and 6. Transverse, sagittal and coronal images (from left to right) of the moving sphere are shown for each scan.

Scan 4a Scan 6a

Scan 4b Scan 6b

Scan 4c Scan 6c

Scan 4d Scan 6d

Scan 4e Scan 6e

Scan 4f Scan 6f

96

Figure 2-31 Visual comparison of moving sphere imaged on PET AC scans with the 4D model: Series 7 and 8 Transverse, sagittal and coronal images (from left to right) of the moving sphere are shown for each scan.

Scan 7a Scan 8a

Scan 7b Scan 8b

Scan 7c Scan 8c

Scan 7d Scan 8d

Scan 7e Scan 8e

Scan 7f

97

2.2.5 Discussion and conclusions

The levels of accuracy of PET-based contours were assessed using the baseline

contours performed on the CT scans. The PET-based contours were found to have a

higher percentage error than the CT-based contours, despite using 3D models to

derive the contours. The image quantification results demonstrate that the standard

whole body PET scanning protocols provide image data which can be extracted by

the TPS tools, with the maximum pixel and SUV data proving more consistent

indicators of uptake than the mean values. Window widths and levels which provide

a geometrically accurate visualisation of different features of the phantom on the

PET images were dependant on background activity and motion. Evaluation of the

moving sphere on both the CT and PET images acquired under “free-breathing”

conditions demonstrate that PET images of a moving object may be able to provide a

4D volume of tumours under the influence of respiration.

CT and PET images of the phantom were acquired with fiducial markers using

standard planning CT and whole-body PET clinical acquisition protocols. Various

patient conditions such as respiration and increasing normal tissue (or background)

uptake were simulated. The activity levels used for acquisition of the PET images

were similar to those in other phantom studies59, 102, 109. The fiducial markers were

easily applied to the CT images but their use was not so straight forward for the PET

images. It took a number of attempts to successfully inject the small amounts of the 18F-FDG and saline solution, so that it was contained within the marker. Despite

only a few drops of 18F-FDG and saline solution being injected into a 5 mm diameter

space the markers visually look larger than this on the PET images. The PET imaged

marker size and the extra time it took to inject the markers needs to be considered in

terms of efficacy of use. While the injection procedure became quicker with practice

and a shielded syringe was used for injecting the markers, it would be expected that

the use of fiducial markers would add to the accumulative annual radiation exposure

for nuclear medicine technologists. Injection times can significantly contribute to

hand exposure due to the emitted positrons from the 18F-FDG110

.

Care must be taken to discriminate between different DICOM formats when the

image data is exported from the PET scanner to DVD. There are two DICOM image

98

export formats available in most scanners. One is a 3D image data DICOM format

with the second format consisting of the separate images of the each slice of the scan

accompanied by DICOM viewing software. The second format is most commonly

provided for referring doctors instead of hardcopy films by the nuclear medicine

technologists. The PET AC and transmission scans were successfully imported into

the TPS using the 3D image DICOM format. Due to the different table feed and scan

acquisition direction between PET and CT, all of the PET images needed to be

flipped as they were imported in the TPS as they were upside down with respect to

the planning CT scans. Flipping these images could be successfully performed

without errors with respect to orientation or distortion of the images.

The CT and PET phantom images of the phantom demonstrate image attributes

described in the literature. The PET images had a much lower spatial resolution that

the CT images as expected50, 60. The voxel sizes of the CT images were 0.15 x 0.15 x

0.30 cm3 or 0.13 x 0.13 x 0.50 cm3 depending on the slice thickness selected when

the scans were acquired. (The variation in slice thickness for the different CT scans

was not intentional; it was due to changes in the set-up of scanning protocols on the

CT scanner). The voxel size of the PET emission and transmission images was 0.4 x

0.4 x 0.4 cm3

.

The level of errors (see Table 2-8) in the volumes of the 3D model-based contours

for both the CT and PET images were related to the way in which the contours were

generated and image voxel size. It is important to note that when the 3D models

were loaded onto both the CT and PET images of the phantom, they were not

adapted. However when the models were converted to contours, this conversion

process appeared to make the contours encompass whole voxels. This explains why

the magnitude of the percentage differences of the volumes for the 3D model-based

contours on the PET images, are larger than those on the CT images. The magnitude

of error for contoured volumes was significantly reduced for objects >1 cm diameter

(i.e. there are more voxels across the diameter of the objects). This effect of voxel

size was seen in Pekar et al’s study using model-based segmentation tools100

.

The contrast resolution of the PET images decreased as the background activity in

the main tank of the phantom increased. The features of the phantom with smaller

99

diameters (the 0.5 cm and 1.0 cm diameter rods) could not be seen as distinct objects

with main tank background activities of 20% and 40%. The ratios of the different

central rod and sphere maximum pixel values to the maximum pixel value of the 3.0

cm rod provide a reason for this. The ratios decrease as the size of these VOIs

decrease and as the activity in the main tank increases (see Table 2-9). This is due to

the effect of object size and TBRs on detection sensitivity due to count recovery

losses, a well documented phenomena of PET imaging56, 57

.

The line plots of the pixel and SUV image data further demonstrate the effect of

object size and TBRs on count recovery losses (see Appendix 1). While SUV values

are normalised to total injected activity and weight, it is also dependant on the uptake

within VOIs. Therefore a reduction in measured activity in imaged VOIs compared

to small objects and increasing background will also affect SUV values109, 111. The

effect of object size and increasing background on SUVs within PET volumes has

implications when considering using an absolute SUV value as a threshold for

contouring. Under-estimates of true volume size would definitely occur if a

threshold > 2.5 SUV is used for contouring VOIs on PET images (as has been

suggested5). The results of several studies have demonstrated this as well as a failure

to contour small PET volumes using this threshold112, 113

.

The results of the PET image analysis demonstrated that the use of thresholds based

on either the maximum pixel or SUV values may actually be more valid. Hoffman et

al56 originally demonstrated that maximum pixel values are a true indication uptake

within a VOI on a PET image. Successive studies6, 109, 111

have demonstrated that

mean estimates of activity within a VOI are heavily influenced by partial voluming

effects and lower background activity (i.e. the ROIs used to determine the mean

value including voxels outside of the VOI). The line plots of the ratios of the

background activity to the activity in the rods and the sphere demonstrate a closer

agreement of the 3.0 cm diameter rod with the true background-activity-ratio when

the maximum values are used compared to the mean values (see Figure 2-25).

The plots of the background-activity-ratio for both the maximum pixel and SUV data

indicate that there may be a linear relationship between these two values as indicators

of imaged activity. The fact that the ratios of the maximum SUV values within the

100

different features of the phantom to the 3.0 cm rod in each image were identical to

those for the calculated using the maximum pixel values provides evidence that this

is the case. This suggests that applying the same threshold value to either the

maximum pixel or SUV value would result in identical contouring results and needs

to be examined as part of the contouring tests.

It was expected that the range of the pixel values in the central rods would be the

same for each of the series of the PET images of the phantom as the same

concentration of activity was maintained for the acquisition of the different images

(except series 2). The reason for this is most likely due to the absence of water in the

main tank of the phantom for series 1 – 4. Water in the main tank would have

attenuated more of the emitted photons from within the rods. The absence of water

in the main tank also accounts for the difference in the SUVs. The surrounding

density of objects affects the calculation of SUVs when attenuation corrections are

applied to emission scans111

.

The scans in series 1 and 2 were not all acquired on the same day which accounts for

the line plots of the pixel data for these series not progressively reducing with each

scan. However the scans in series 4 were acquired on the same day in sequential

order (i.e. scan (a) first to (g) being the last). The first scan attempted failed to

reconstruct and scans c, d, and e in the series show a dramatic jump in counts and

SUV. There also appears to be a reconstruction error in the series 8 images. On the

sagittal and coronal views (see Figure 2-23) it seems as if the couch position changed

during scan acquisition. This was not the cause as this effect is seen in all scans the

scans in this series. Due to limited access time on the PET scanner, the series 4 and

8 scans were not repeated.

Due to the variation in the pixel values across the different PET images of the

phantom, a windowing scale that was relative to the maximum image value was

more easily applied and effective in assessing appropriate viewing windows for these

images. Using the CT images of the phantom as a visual reference proved effective

in determining window widths and levels which resulted in geometrically accurate

visual representations of the different features of the phantom for the PET images.

As the PET transmission scans images are density-based, a universal viewing

101

window of WW = 40% and WL = 20% can be applied to these images. However

viewing windows for PET AC images were found to be affected by background

activity levels and the presence of motion during scan acquisition.

Despite these findings, a general PET viewing window of WW = 20% and WL = 0%

(relative to the image maximum) was suitable for displaying both the background

uptake and the central rods and could be applied to almost all the different simulated

patient conditions in the phantom images. This viewing window was not suitable for

accurately displaying the dimensions of the central rods when the background

activity was 40% or for viewing the moving sphere. A WW = 20% of the maximum

and a WL = 5 – 10% displayed the central rods without the background uptake (again

except when the background activity was 40% and for viewing the moving sphere).

This second viewing window is similar to Hong et al114

findings; narrowing the WL

to within 10 – 15% of the WW allowed the central rods to be well defined with

reasonably sharp edges.

A WW = 10% and WL = 0% was found to be suitable for viewing the moving sphere

based on visual correlation of the modelled contour of the 4D volume of the moving

sphere. This qualitative assessment demonstrated that the moving sphere was

imaged over its full range of motion on the PET images of the phantom, except when

the activity in the main tank relative to the central rods and sphere increased above

20%. The mean pixel and SUV values were considerably lower for the moving

sphere compared to those for the static sphere which was indicative of the much

lower values observed at the periphery of the imaged moving sphere (also observed

by Caldwell et al70

).

As expected the series of “free- breathing” CT scans of the phantom where the

sphere was moving did not image the true range of motion of the sphere. Chen et al7

state that the lengthening or shortening of an object moving in the direction of the

long axis of the couch (or the z-axis of the final image) is due to the phase of the

object motion relative to the imaging plane of the scanner. The spiral shaped

distortions of the sphere’s dimensions in the transverse image plane (shown in

Figures 2-18 and 2-19) are due to the motion of the sphere, the helical acquisition of

the CT data on the Siemens MSCT scanner and the interpolation between the

102

detector rows of the CT scanner115. The differences in the imaged dimensions of the

moving sphere between the PET and CT images has implications with respect to

accurately registering the images and defining a 4D PET-based target volume.70

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2.3 Patient image acquisition and analysis

2.3.1 Aims

1. To acquire planning CT and PET images for adequate numbers of patients

undergoing radiation therapy that could be used to test image registration and

GTV delineation protocols determined from the phantom data.

2. To determine appropriate clinical protocols for acquiring patient PET scans that

are suitable for use in the radiotherapy treatment planning process. These

protocols would ensure that:

• The patient was in the same position that would be required for their CT

planning scan and radiotherapy treatment.

• Both the AC emission and transmission PET images would be able to be

imported into the TPS for registration with the planning CT scan

3. Analyse each patient’s PET images using techniques defined on the phantom

PET images to determine:

• Maximum count and SUV data in regions of high uptake that correspond

to malignant disease.

• A background (normal tissue) uptake level.

4. To evaluate the application of the PET viewing window protocols determined

from the phantom image tests, to patient images.

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2.3.2 Methodology

2.3.2.1 Estimation of patient image numbers for protocol trials

The desired outcomes of this research project were that the protocols used as part of

the RT image registration and RO tumour volume definition trials would show high

levels of precision and reproducibility. Sufficient numbers of patient data sets were

required for the study to be able to demonstrate a clinically significant mean

difference for both the registration and contouring results. Taking into account the

uncertainty margins applied to GTVs for lung cancer at the time the project was

started, it was desirable for the protocols to demonstrate that:

• Different RTs could register CT and PET images with a 5 mm mean

difference from a baseline registration.

• Different ROs could define a volume on a PET scan registered with

planning CT scan with a 5 mm mean difference in any direction (superior,

inferior, right, left, anterior and posterior).

The required mean levels of difference and the limitation on the number of available

ROs for participation in the contouring trials were factors when an estimate of the

number of patient images required for the protocols trials was made. It was

determined that:

• 20 patient PET and planning CT scans would need to be acquired

• 6 RTs would be required to register CT and PET images

• 6 ROs would be required to define the tumour volumes.

• These numbers of patient image data sets and participants would result in:

A mean difference of 5 mm demonstrated between the

participants’ results for each patient with α = 0.05

A 95% probability of detecting a true difference between the

image registration and the contouring methods.

2.3.2.2 Clinical protocols for acquiring patient PET scans

Only PET scans where the patient was scanned in the same position required for their

planning CT scan were to be used for the image registration and GTV delineation

105

trials. Both the AC emission and transmission PET images for these patients would

be required as part of the image registration trials.

A process for identifying patients that were possible candidates for radiotherapy

needed to be established. This was achieved by attending the combined oncology

clinic where lung cancer patients would present for their treatment management

options (surgery, chemotherapy and/or radiotherapy). Initial inquiries highlighted

the fact that a PET scan was usually requested at the time of the patient’s initial

presentation at this clinic to verify the stage of their disease.

A system was set up to flag potential radiotherapy patients to the nuclear medicine

technologists performing the PET scans. A sticker was attached to the PET request

form highlighting that the patient was being considered for radiotherapy along with a

request for the AC emission and transmission scans to be burnt on CD in the correct

DICOM format (the PET transmission scans were not routinely archived once the

AC emission scan was reconstructed). The nuclear medicine technologists were

contacted and the patient positioning requirements were discussed. A flat couch top

and the arm stabilisation device and a head and neck rest was on site at the scanner.

A standard patient position on the couch using the radiotherapy stabilisation devices

was used for the identified patients. Assistance with the patient radiotherapy

positioning requirements was provided onsite at the PET scanner.

2.3.2.3 PET scan acquisition

The routine scanning procedures for staging lung cancer in use in the nuclear

medicine department throughout the duration of this project were used for the PET

scans of each identified patient as per ethics approval. Additionally, the PET scans

of the phantom were based on these routine scanning procedures including selecting

the same scanning and image reconstruction protocols. These scans were able to be

successfully imported, manipulated and quantified in the TPS.

All patient PET scans were acquired on a Philips Allegro stand-alone PET scanner

using the following procedures:

106

• The patient was weighed, with their weight recorded and entered as part

of the scan acquisition and reconstruction parameters so that SUV data

was available on the AC emission image.

• The patient was injected with a solution of saline and 18F-FDG one hour

prior to scan acquisition. Injected activity was varied for each patient to

account for their weight and source decay to ensure adequate

concentration and accumulation of 18

• Patient position:

F-FDG during the scan.

Supine on flat couch top using the radiotherapy stabilisation

equipment in their intended CT planning and treatment position.

• Entered scan orientation parameters:

Supine, feet first, table direction moved out, patient scanned from

head to feet

• Scan length:

The level of the external auditory meatus (EAM) to the level of

the patient’s mid thigh region

• FOV = 576 mm

• Frame acquisition:

Number of frames dependant on patient height

Emission scan frames: scan time = 3 mins/frame

Transmission scan frames: scan time = 1.54 mins/frame


144 x 144 mm image matrix size

Reconstruction algorithm = body/RAMLA 3D/3D AC/SUV

No fiducial markers were used in any of the patient PET scans due to concerns that

the activity in the markers could be mistaken for metastatic disease (see ethical

considerations in Chapter 1).

2.3.2.4 CT scan acquisition

The routine planning CT scanning procedures for lung cancer in use at the

radiotherapy department throughout the duration of this project were used for

107

scanning the patients who did proceed to commence radiotherapy after their staging

PET scan as per ethics approval.

All the patient planning CT scans were acquired on a Siemens Somatom “Open

Sensation” 20 slice, wide bore CT scanner using the following procedures:

• Patient position:

Supine on a flat couch top using the radiotherapy stabilisation

equipment in their intended CT planning and treatment position.

• CT ball bearing fiducial markers were placed on the patient’s anterior

skin surface on midline at the superior-inferior level of their known

primary disease.

• The scanner lasers were aligned with the fiducial markers and the couch

was zeroed at the alignment point of the lasers and the markers

• Entered scan orientation parameters:

Supine

Head first

Table moves into scanner

• Scan length:

From the inferior aspect of the chin to 5 cm below the inferior

aspect of both lungs.

• 3 mm slice thickness

• FOV = 750 mm

• The standard chest protocol scanning parameters was selected:

70 mAs

120 kVp

Pitch = 1.2


512 x 512 mm matrix size

Siemens adaptive multiplanar reconstruction (AMPR) algorithm

• The patients were instructed to breathe normally throughout the scan.

The “free breathing” CT scans of the phantom were based on the scanning

procedures detailed above.

108

2.3.2.5 Image analysis

Each of the patient PET and CT images were imported and registered in the

Pinnacle3 TPS. The method of image quantification developed on the phantom PET

scans was applied to patient PET AC images to analyse the background-activity

ratios on the patient data. To extract the maximum pixel or SUV values from the

patient images a 3D model of a sphere (already existing in the MBS tools in

Pinnacle3

) was loaded and positioned over the various volumes and regions of

interest within the patient (see Figure 2-32).

Figure 2-32 Method for obtaining image data from the PET AC patient images The images demonstrate the 3D model generated contours used to obtain image data from the tumour (yellow contour), normal lung tissue (purple contour) and the liver (blue). The images shown are different orientations at the level of the primary tumour. The orientations of the images are: the top left image is a transverse, the bottom left image is sagittal and the image on the right is coronal.

This model was expanded and rotated to cover the intense uptake regions

corresponding to malignant lung cancer identified on the radiologist’s report for each

109

patient’s PET scan. The model was then converted into a 3D contour so that the TPS

ROI tools could be used to obtain the maximum pixel and SUV data for the primary

tumour and nodes of each patient. Background (normal tissue) uptake in both the

lung and liver was determined by loading the 3D model of a sphere onto the liver and

disease free regions of the ipsilateral lung on the PET images. Lung-based and liver-

based background-activity ratios relative to the GTV or involved lymph nodes were

calculated for each patient’s PET AC image.

2.3.2.6 Application of phantom-based image windowing results

The image windowing results for viewing PET images that had been determined

using the phantom PET images were tested on the patient PET images (refer to

Figures 2-26 and 2-27). The lung or liver background-activity ratios for the patient

PET AC images were equated with the known percentage of activity in the main tank

of the phantom used in the different PET scan series. The patient background-

activity ratios were extrapolated to the nearest background percentages of the

phantom PET AC images to determine the viewing windows that should be applied

for each patient image.

Different window widths and levels were applied to demonstrate various features on

the AC scans based on the type of tissue surrounding the primary or nodes for each

patient. If a primary tumour or any involved nodes were surrounded predominantly

by lung tissue then the lung-based background-activity ratio was used to apply

viewing windows to the patient’s PET AC image. The liver-based background-

activity ratio was used to apply viewing windows if the tumour or involved nodes

were predominantly surrounded by non-lung tissue. The same viewing window that

was applied to all the phantom transmission scans the patient transmission scans as

uniform viewing window had been established for the phantom transmission images.

110

2.3.3 Results

2.3.3.1 Patient identification and PET scan acquisition protocols

Due to time constraints, the sample size of the 20 patient planning CT and PET

images was not able to be reached. Only 14 patient PET scans were acquired over a

15 month period (August 2004 to November 2005) for whom it was anticipated that

a 3D conformal treatment plan would be required. All of these patients were

positioned on the PET scanner, supine on a flat couch top using the radiotherapy

stabilisation equipment in their intended CT planning and treatment position.

5 of the patient’s images could not be used in the image registration and GTV

definition trials as they did not progress to have a planning CT scan. The results of

their PET scan changed the planned treatment management of these patients and are

summarised in Table 2-11. The remaining 9 patients who did proceed to have a

planning CT scan were positioned for that scan in the exact same position as they

were for their staging PET scan. Table 2-11 provides the clinical history of these 9

patients.

Table 2-11 Patients whose PET images were not used in the clinical trials

Excluded patient Reason for exclusion from the study

1X The patient was being considered for combined chemo and XRT if they had nodal involvement. The PET scan indicated no nodal involvement. The patient had a Lt lower lobe T1N0M0 NSCLC primary that was managed via surgical resection.

2X The PET scan demonstrated multiple metastases. The patient’s stage was upgraded to T4N0M1 with a small Lt upper lobe nodule NSCLC and metastases in the Rt femur and Lt humerus. The patient was managed with chemotherapy only.

3X The patient’s stage was T3N2M0 for a right lung NSCLC following their PET scan and was being considered for combine chemo and XRT. Their planning CT scan demonstrated a collapsed right lung and the patient did not proceed with radiotherapy.

4X The patient’s PET scan demonstrated metastases and the patient was managed with palliative XRT in which no planning CT scan was required for their dosimetry.

5X The patient’s PET scan demonstrated metastases and the patient was managed with palliative XRT in which no planning CT scan was required for their dosimetry.

111

Table 2-12 Summary of the patient clinical data whose images were acquired for the image registration and GTV definition trials

Patient Trial No Bronchoscopy Biopsy Histology Site of disease from PET scan Stage

after PET scan

Treatment following PET PET AC scan

PET transmission

scan

1 Yes Yes NSCLC (undifferentiated)

Recurrence in Rt upper chest wall and upper Rt mediastinum T3N2M0

XRT for local control (Patient had induction chemo and surgical resection 2 years prior to current presentation).

Yes No

2 Yes Yes NSCLC (SCC) Anterior segment of the Lt upper lobe and evidence of Lt hilar nodal involvement T2N1M0 Chemo/XRT (Not a suitable candidate for

surgery). Yes Yes

3 Rt thoracotamy Yes NSCLC 5x7cm subcarinal nodal mass and a Lt pulmonary nodule that was not FDG avid T1N3M0 Palliative Chemo/ XRT Yes No

4 Yes Yes NSCLC (SCC) Anterior Lt upper lobe tumour and Lt pulmonary hilar node involvement. T2N3M0 Chemo/XRT Yes Yes

5 Yes No NSCLC

Extensive malignant mediastinal lymphadenopathy – paratracheal region, anterior and superior mediastinum on the right and in the subcarina to midline. Mild FDG uptake in the right pleural effusion demonstrated on CT.

T2N2M0 Chemo/XRT Yes Yes

6 Yes Yes NSCLC (SCC)

Rt lower lobe – intense uptake in the right postero-inferior lung. No definitive evidence of mediastinal spread however lower grade focal activity in the mediastinum was noted.

T2N2M0 Chemo/XRT Yes No

7 Yes Yes NSCLC (SCC)

Lt upper lobe lesion with involvement in the aorta pulmonary nodes with a suspicious malignant lymph node in the contralateral Rt hilum.

T1N2M0 Palliative Chemo/XRT (Not a suitable candidate for surgery). Yes No

8 No Yes NSCLC Intense uptake in the Rt mid-zone lesion (1cm diameter) with no evidence of hilar or mediastinal nodal involvement.

T1N0M0 XRT (Patient unfit for surgery due to co-morbidities and poor performance status) Yes Yes

9 Yes Yes NSCLC

Intense uptake in a large Lt upper lobe tumour, with extension into the Lt lung apex. The mass was also abutting the Lt hilum and extending into mediastinal structures, but not beyond midline.

T4N2M0 Chemo/XRT Yes Yes

112

2.3.3.2 Image analysis

Lung-based or liver-based background-activity ratios (relative to the GTV or

involved lymph nodes) were determined for each patient’s PET AC image data (see

Table 2-12). The SUV data was not able to be extracted from the PET AC images of

all of the patients. The data in Table 2-12 is based on the maximum pixel values

obtained from the different GTV, lung and liver VOIs.

Table 2-13 Patient GTV, Liver and Lung VOI data

Patient GTV VOI max pixel value

Liver VOI max pixel value

Lung VOI max pixel value

Background-activity ratios Liver-based Lung-based

1 3253 2492 1142 76.6 35.1

2 7944 1731 735 21.8 9.3

3 8022 2212 692 27.5 8.6

4 13288 3123 1369 23.5 10.3

5 16003 3107 1714 19.4 10.7

6 22246 3750 1131 16.9 5.1

7 11263 3101 1202 27.5 10.7

8 13002 3952 1267 30.4 9.7

9 55009 2626 863 4.8 1.6

2.3.3.3 Application of phantom-based image windowing results

Window widths and levels were applied to each patient PET AC image to view the

background and GTV, or the GTV alone, using either the lung or liver-based

background-activity ratios depending on the location of the primary or involved

lymph nodes. Figures 2-33 to 2-35 indicate the window widths and levels applied to

each image and the resulting images. The viewing windows used for patient 1’s PET

AC image were determined based on assumptions made from the phantom PET AC

viewing window results. Patient 1 had two involved lymph nodes in the right upper

chest wall and mediastinum; therefore the liver-based background-activity ratio was

more appropriate to use for applying the viewing windows for this patient. The value

of this ratio (76.6) was almost double the maximum main tank percentage activity

scanned for the phantom (40%). The WW and WL trends for the PET images with

main tank activity 20% and above were followed (i.e as the main tank activity

doubled, 0.1 was added to WW, the WL was kept the same).

113

Figure 2-33 PET image viewing window results: Patients 1 - 3

Patient 1 viewing windows based on non-lung tissue (liver) % of uptake relative to the involved lymph nodes

Patient 1 Fusion WW 0.4 WL 0.0 (%max)

Patient 1 PET AC WW 0.4 WL 0.0 (%max)



Patient 2 Fusion

WW 0.2 WL 0.0 (%max) Patient 2 PET AC

WW 0.2 WL 0.0 (%max) Patient 2 Fusion


WW 0.2 WL 0.1 (%max) Patient 2 PET trans

WW 0.4 WL 0.2 (% max)

Patient 3 viewing windows based on non-lung tissue (liver) % of uptake relative to the involved the primary tumour





114

Figure 2-34 PET image viewing window results: Patients 4 – 5

Patient 4 Fusion





WW 0.4 WL 0.2 (% max)

Patient 5 Fusion





WW 0.4 WL 0.2 (% max)





115

Figure 2-35 PET image viewing window results: Patients 7 – 9





Patient 8 Fusion





WW 0.4 WL 0.2 (% max)

Patient 9 Fusion





WW 0.4 WL 0.2 (% max)

116


Only 9 out of the 14 patients, whose whole-body staging PET scans were obtained

with the patients in their potential radiotherapy treatment position, continued with

their intended 3DCRT treatment planning. Of the 5 patients who were excluded

from the image registration and GTV definition trials, one was down-staged and

underwent surgical resection of the primary tumour instead of 3DCRT. Distant

metastases demonstrated on the PET scans were the most common reason for

changes in the treatment management of the remaining 4 patients. Mac Manus et al27

also reported 30% of patients with NSCLC being considered for radiotherapy were

upstaged due to distant metastases findings on their 18

F-FDG PET staging scans.

It was decided to commence the image registration and GTV definition trials using

the 9 patient PET and CT image data sets due to time constraints and the referral

rates of the lung cancer patients who presented during this time. It had been

estimated that the images of 20 patients with 6 RTs and ROs participating in the

trials would provide statistically significant results. The number of RTs participating

in the image registration trials was doubled to 12 to counteract the effect of halving

of the number of patient image data sets. The number of participating ROs in the

GTV definition trials remained at 6, limiting the contour protocol test to a proof of

concept study.

A protocol was successfully implemented which identified patients in the early

stages of their clinical management as potential candidates for 3DCRT who would

benefit from CT-PET fusion. Alerting the nuclear medicine department of

radiotherapy positioning and image data requirements prior to their whole-body PET

scan via the request slip proved effective. Pan et al’s 116

discussion of some of the

issues of incorporating PET/CT into radiotherapy highlights the importance of

collaboration between nuclear medicine and radiation oncology departments to

achieve accurate patient positioning. Development of a co-ordinated

multidisciplinary approach across multiple departments and institutions was crucial

to ensure that a single PET scan met both the nuclear medicine and radiotherapy

scanning requirements.

117

Practically, collaboration between the nuclear medicine and radiotherapy

departments (which were located in different hospitals) required inter-disciplinary

education. Radiation therapists needed to gain an understanding of whole-body PET

scanning procedures and nuclear medicine technologists needed to understand the

importance of patient positioning in radiotherapy. Initially a radiation therapist

would attend the patient’s PET scan to position the patient in the correct position

using the radiotherapy stabilisation equipment. After the first couple of patients were

scanned the nuclear medicine technologists were sufficiently familiar with the

radiotherapy positioning requirements to confidently position the patients

themselves.

It was important that the correct terminology was used on the PET request form so

that the images supplied on the DVD contained both the attenuation-corrected and

transmission PET images in the 3D DICOM format (particularly as the PET scanner

was off-site). Some of the patient image data sets acquired for this study did not

include transmission scans or SUV data, which was not able to be retrospectively

acquired. However as experience increased in acquiring whole-body PET scans

which were suitable for use in radiotherapy, this was no longer an issue.

As the SUV image data was not available for all the PET AC images, the lung and

liver-based background activities were determined using the pixel values. Based on

the conclusions of the phantom viewing window tests, the ratio of the activity in the

normal tissue immediately surrounding an object were used to select viewing

windows. Maximum pixel values within the tumour and its surrounding normal

tissues were used to determine background-activity ratios (as per the phantom image

quantification results and the literature56

supporting this value as a true indication of

uptake).

The uptake in the normal mediastinal tissues proximal to the primary tumour or

involved lymph nodes in this region of the chest would seem a more appropriate

choice to determine a background-activity ratio rather than a liver-based ratio.

Paquet et al’s117 study of within-patient variability of 18F-FDG uptake demonstrated

that uptake in normal mediastinal tissue is equivalent to that in normal liver tissue.

Due to difficulties in sampling the uptake in the normal mediastinal tissues without

118

including uptake from either the left ventricle or the lung cancer itself, the maximum

pixel values within a volume of interest within the liver was used as a substitute for

mediastinal uptake. It is important that the proximal lung tissue is used to determine

background-activity ratios for primary tumours or involved lymph nodes surrounded

by lung tissue. Wang et al118

found that uptake is non-uniform between the right and

left lungs and the different lobes of the same lung.

The viewing window protocols from the phantom image tests were easily applied to

the patient PET images. A WW=40% and WL=20% was applied to each PET

transmission image as per the phantom image test results. Lung-based background-

activity ratios were used to select the viewing windows for 6 of the 9 patient PET AC

images. Liver-based background-activity ratios were applied to the AC images for

patients 1, 3 and 9 where the primaries or involved lymph nodes were mostly

surrounded the mediastinum or chest wall. The general PET viewing window to

display both the background and tumour uptake of WW = 20% and WL = 0% was

applied to all but patient 1’s PET AC images (i.e. the background-activity ratios were

less than 40%).

A WW = 20% and a WL = 10% to display the tumours without the proximal

background uptake was applied the AC images for patients 2 – 9. While the

phantom image tests found a WL = 5 – 10% for the different background ratios, no

visible difference could be found in applying either a 5% or 10% WL to the patient

images. As Hong et al’s114

study based on patient images found that narrowing the

WL to within 10% of the WW allowed tumours to be displayed with reasonably

sharp edges which correlated with their dimensions on CT, this approach was taken

to simplify the application of the phantom viewing window results to patient images.

The phantom images did not simulate every possible patient specific condition hence

some assumptions were made when applying viewing windows to the patient images.

These assumptions involved extrapolation of the results for the imaged phantom

conditions, which was demonstrated by the choice of viewing windows chosen for

displaying patient 1’s AC images. The phantom viewing window tests demonstrated

that the window levels for viewing the moving sphere required a WW=10% and

WL=0%. While the entire chest is under influence of respiration, it needs to be noted

119

that the moving sphere was surrounded by air on the phantom and that none of the

tumours in the patient images were surrounded by tissues with 0% background

activities. Taking these factors into account it was assumed that motion effects

would be counterbalanced by the fact that that the viewing window tests also

demonstrated that as background increased, WW increased.

120

3

3.1 Phantom based image registration technique evaluation

Image registration technique evaluation and

development of a protocol for the clinical trials

3.1.1 Aims

Various processes can be used when registering CT and PET images. Using the

previously acquired PET and CT scans of the phantom, the following will be

evaluated:

1. The relative level of accuracy of the CC and MI image registration algorithms.

2. The level of accuracy and reproducibility of the following registration

techniques:

• Using the MI algorithm to auto-register the PET AC scan with the

planning CT scan, compared to,

• Using the MI algorithm to auto-register the PET transmission scan with

the planning CT scan and using the post-registration parameters of the

transmission scan to register then PET AC scan (transmission scan-based

registration).

3. The effects of the differences in the imaged dimensions of the moving sphere

between the PET and CT images on the level of accuracy and reproducibility of

automated registration techniques.

4. The efficacy of fiducial markers in the image registration process for confirming

the accuracy of registration outcomes by localising the fiducial markers placed on

the phantom for each of the CT and PET scans of the phantom on pre-registered

image data sets of the phantom.

121

3.1.2 Methodology

3.1.2.1 Algorithm tests

The phantom PET AC scan 1a (scan (a) from series 1 – see Table 2-4 Chapter 2) was

imported twice into the same plan in the Pinnacle

Reproducibility tests of the automated registration software

3

TPS creating a perfectly registered

pair of images. Using the Syntegra Image Fusion platform of the TPS, one of the

images (the secondary data set) was offset from the other (the primary image data

set) using translation and rotation offsets about each of the image axes.

Table 3-1 Pre-registration offsets of secondary image from the primary for algorithm reproducibility tests

Condition

Secondary image pre-registration start point offsets from the origin

Translation (cm) offset Rotation (degrees) offset

x y z x y z

1 2 2 2 1 1 1

2 -2 -2 -2 -1 -1 -1

The CC algorithm was used to automatically register the two data sets of the same

image for two different pre-registration offset conditions (see Table 3-1). Once the

Syntegra software had registered the two data sets, the x, y and z axes translation and

rotation parameters of the secondary image relative to the primary were recorded

post-registration. This process was then repeated so that a total of 5 registrations for

each pre-registration offset were performed.

The entire process of offsetting the data sets prior to registration for conditions 1 and

2 and registering the data sets from these offsets 5 times was then performed using

the MI algorithm.

122

The CC and MI algorithms were used to register the following data sets:

Cross correlation and mutual information algorithm tests

1. CT1 and CT1

• Two copies of the same CT scan of the phantom (scan no.1 from the static

series of CT scans – see Table 2-2 Chapter 2)

2. PET AC 1a and PET AC 1a

• Two copies of the same PET AC scan of the phantom (Scan (a) from PET

series 1 – see Table 2-4 Chapter 2)

3. PET AC 1a and PET AC 1b

• Two PET AC scans of the phantom from the same series (Scan (a) and

scan (b) from PET series 1 – see Table 2-4 Chapter 2)

Initially each of the secondary data sets for the pairs of data sets above was offset

with translation parameters for the x, y and z axes only (conditions 1-10 of Table 3-

2) prior to registration. The data set pairs were then registered using the Pinnacle3

Syntegra software, using the CC and then the MI algorithms.

Table 3-2 Translation only pre-registration offsets of secondary image from the primary for algorithm accuracy tests

Condition



x y z x y z

1 1 1 1 0 0 0 2 -1 -1 -1 0 0 0 3 2 2 2 0 0 0 4 -2 -2 -2 0 0 0 5 3 3 3 0 0 0 6 -3 -3 -3 0 0 0 7 4 4 4 0 0 0 8 -4 -4 -4 0 0 0 9 5 5 5 0 0 0 10 -5 -5 -5 0 0 0

Rotational offsets for the x, y and z axes were then added to pre-registration

translation offsets. For each of the translation offsets used previously, rotation

123

offsets were applied, starting at 0.5° and increasing to 2.0° in 0.5° increments. Extra

rotation offsets were applied for the translation offsets of 5 and -5 (see Table 3-3).

Table 3-3 Translation and rotation pre-registration offsets of secondary image from the primary for algorithm accuracy tests

Condition



x y z x y z

1a-d 1 1 1 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 2a-d -1 -1 -1 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 3a-d 2 2 2 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 4a-d -2 -2 -2 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 5a-d 3 3 3 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 6a-d -3 -3 -3 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 7a-d 4 4 4 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 8a-d -4 -4 -4 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d)

9a-g 5 5 5 0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)

0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)

0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)

10a-g -5 -5 -5 0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)

0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)

0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)

3.1.2.2 Baseline registrations

Pairs of CT and PET images of the phantom listed in Table 3-4 were imported into

Pinnacle3

for registration, with the CT data set always designated as the primary

image and the PET scan as the secondary image. The CT and PET data sets were

paired according to phantom conditions (i.e. main tank full or empty, the presence of

the sphere, or whether the sphere was static or moving). Note that when a free

breathing series of CT scans was registered with a PET series, each scan in the free

breathing CT scan series was registered with each AC scan in the PET series.

Baseline manual registrations were performed for each of the pairs of data sets,

ensuring that the main tank of the phantom was correctly registered in each case.

The post-registration translations and rotations about the x, y and z axes were

recorded for each manual registration.

124

Table 3-4 Registered CT and PET AC scans of the phantom

CT scans of phantom (Primary data set)

PET AC scan no of phantom from the different series of PET scans

(Secondary data sets) registered with each primary CT scan

Static scan no 1

The main tank alone with the central rods filled with water and the main tank empty 1a 1b 1c 1d 1e 1f

Static scan no 1

The main tank alone with the central rods filled with water and the main tank empty 2a 2b 2c 2d 2e 2f

Static scan no 3

The main tank with the central rods filled with water and the main tank empty and the sphere filled with water in the static position

3a 3b 3c 3d 3e 3f

Free breathing Series 3

scans

5 scans with the main tank with the central rods filled with water and the main tank empty and the sphere filled with water oscillating at 20cycles/min

Scan 1 4a 4b 4c 4d 4e 4f





Static scan no 2

The main tank alone with both the central rods and the main tank filled with water 5a 5b 5c 5d 5e 5f


scans

5 scans with the main tank with both the central rods and the main tank filled with water and the sphere filled with water oscillating at 20cycles/min







scans








scans


Scan 1 8a 8b 8c 8d 8e





125

3.1.2.3 Auto-registration of the planning CT and PET AC scans using the MI

algorithm

The same pairs of CT and PET image data sets (as per Table 3-4) were imported into

separate plans. Prior to automatically registering each pair of images, the PET AC

scan was centred on the CT scan (i.e. the midpoint of the 3D image coordinates of

each were aligned). This ensured that there was sufficient overlap of the two data sets

prior to registration. Each of the pairs of data sets were then registered using the

Syntegra automated registration software, using the MI algorithm. The post-

registration x, y and z axes translation and rotations were recorded.

3.1.2.4 Automated PET transmission scan-based registration with the planning

CT scan using the MI algorithm

The same pairs of CT and PET image data sets (as per Table 3-4) were imported into

separate plans, as well as the associated PET transmission scans for each of the AC

scans. This resulted in the primary CT scan of the phantom as well as two secondary

PET images in each plan. The procedure for registering the transmission scan then

the AC scan was as follows:

1. The transmission scan was nominated as the secondary data set

2. The transmission scan was then centred on the CT scan

3. The transmission scan was then registered with the CT scan using the Syntegra

software and the MI algorithm.

4. The post- registration x, y and z axes translations and rotations were copied to the

AC scan, resulting in registration of AC scan with the CT based on the

registration results using the transmission scan.

The post-registration x, y and z axes translation and rotations for these registrations

were recorded.

3.1.2.5 Fiducial marker tests

To test the accuracy of the fiducial markers for point based registration and

confirming registration, the following series of baseline registrations were used:

126

• Static CT scan no 1 and PET Series 1 AC scans


• Free breathing CT scan series 3- scan 1 and PET Series 4 AC scans




For each of the manual baseline registrations above, the Syntegra point-based

registration tools were used to localise the centre of the two fiducial markers placed

on the phantom during the acquisition of each CT and PET scans. The following

steps were used to localise the centre of the imaged fiducial marker on each of the

registered CT and PET AC scans:

1. Two sets of fiducial marker “point pairs” were created for each registered CT

and PET AC scan (i.e. one point pair for the right marker and one point pair for

the left marker)

2. The centre of the left and right fiducial markers (placed on top of the phantom)

was localised on the CT scan of the phantom by placing the first point of each of

the point pairs in the centre of each of the fiducial markers. The centre of the

marker is the 5 mm axial hole in the middle of the IZI MM3003 multi-modality

fiducial markers on the slice with the z coordinate = 0 (the CT couch coordinates

were zeroed in the sup-inf (z coordinate) direction when the phantom was CT

scanned.

3. The centres of both the left and right fiducial markers were then localised on the

registered PET AC scan of the phantom. The centre of each of the markers was

defined by scrolling through the slices of the PET AC scans in the sup-inf

direction and determining the slice half-way between the most superior and

inferior slices that the fiducial marker was imaged on. The point was then placed

in the middle of the intense region where the 18-FDG was injected into the 5 mm

axial hole of each marker on this slice.

127

3.1.3 Data analysis

3.1.3.1 Cross correlation and mutual information algorithm tests

The x, y and z axes translation and rotation parameters of the secondary image

relative to the primary were recorded post-registration. These results were converted

to absolute values. Scatter plots of the absolute values were generated so that the

effect of increasing the pre-registration translation and rotation offsets on the

magnitude of the post-registration translation and rotation parameters using different

registration algorithms could be evaluated.

3.1.3.2 Comparison of auto-registration results of the phantom CT and PET

images

The post-registration translation and rotation values for each of the image axes for

each set of CT and PET image registration were subtracted from the baseline

registrations of the same pairs of data sets. This provided the difference in the post-

registration x, y and z translation and rotation parameters between the baseline

registrations and each of the automatic registrations.

Calculation of the post-registration results relative to the baseline registrations

Box plots of the post-registration translation and rotation parameters relative to the

baseline registrations were generated to demonstrate the spread of the automated

registration results relative to the baseline registrations. The registration results were

grouped by the PET scan series of the phantom as each series was acquired with

varying “patient specific” conditions. Where each of the 5 scans in a free breathing

CT scan series was registered with a PET series in which the sphere was moving

during acquisition (see Table 3-4), box plots were created with the registrations

results grouped by the CT scan. Series of box plots were grouped by the following

registration results:

Box plots of the distribution of the post-registration results relative to the

baseline registrations

• Intra-PET series results for the registrations where the CT and PET AC

scan only were used for automatic registration.

128

• Intra-PET series results for the registrations where the PET transmission

scan registration parameters with the CT scan formed the basis of the PET

AC scan registration.

• Combined registration results from the series where the components of

the phantom were static for:

The PET AC scan-based automatic registrations

The PET transmission scan-based automatic registrations

• Combined registration results from the series where the sphere was

moving for:

The PET AC scan-based automatic registrations

The PET transmission scan-based automatic registrations

The mean and standard deviation of the post-registration differences from the

baseline registrations x, y and z axes were calculated, with the data grouped by

registrations for the different PET scan series of the phantom. This data was used to

create scatter plots of standard deviation against the mean as described by Bland and

Altman

Plots of the standard deviation against the mean for the registration results

119

• Assessment that there is not a systematic error in the repeated registration

of CT and PET images for the same phantom conditions (i.e. as the mean

difference from the baseline registrations increases the standard deviation

should not increase).

and using the “true value is constant” method. This allows for:

• A graphical means of assessing the level of reproducibility of the different

registration methods.

The closer the standard deviation for a group of registrations is to

zero the more reproducible the method.

As this method assumes that the true value (in this case the post-registration results)

should be constant, there is no pairing of the data between the different registration

methods. The following plots of the standard deviation against the mean for the

differences from the baseline registrations were created for:

129

• The post-registration x, y and z translations and then the post-registration

x, y and z rotations for:

The combined PET AC scan-based registrations

The combined PET transmission scan-based registrations


x, y and z rotations where the components of the phantom were static for:

The PET AC scan-based registrations

The PET transmission scan-based registrations


x, y and z rotations where the sphere was moving for:

The PET AC scan-based registrations

The PET transmission scan-based registrations

The repeatability coefficient as described by Bland and Altman

Calculation of the repeatability coefficient 120

was used to

quantify the level of reproducibility of the registration results. The repeatability

coefficient (rpt coeff) is defined as:

ws 21.96 coeffrpt =

= 2.77 s

where sw

w

= the within subject standard deviation

The within subject standard deviation is obtained by:

1. Performing one way analysis of variance with the subject as the factor

2. The mean of the within subject variances for a method is then determined

to obtain sw

2

The repeatability coefficient provides the level of magnitude that any two

registrations of the phantom CT and PET images will be within. The magnitude of

the coefficient is relative to the units of the registration parameters (i.e. centimetres

and degrees for the post-registration translation and rotation parameters

respectively). Repeatability coefficients were calculated with the registrations results

130

grouped by the different image based registration techniques and the presence or

absence of the moving sphere in the registered images.

The mean and standard deviation was calculated using the absolute values for each of

the post-registration translation and rotation parameters. The registration results

from all the intra-PET series registrations were combined. A paired t-test was used

to compare the means for each of the post-registration parameters between the AC

scan and transmission scan-based registration methods. To determine whether

motion influences registration outcomes, a t-test was performed comparing the intra-

PET series registration results where all the phantom components were static to the

intra-PET series registration results where the sphere was moving. The t-tests were

performed separately for the AC scan-based and transmission scan-based registration

results.

Comparison of the mean registration results


The centres of the fiducial markers on the CT scan of the phantom were able to be

accurately located due to the method of acquisition of the CT images of the phantom.

As each CT image was acquired, the couch longitudinal position was “zeroed” at the

centre of the fiducial markers. This resulted in a slice being imaged through the

centre of the markers and as well as an image coordinate of z = 0 corresponding to

the centre of the fiducial markers on the CT images. With the PET AC images

registered with each CT scan the point based Syntegra software tools were used to

analyse the accuracy of the fiducial marker localisation on the PET AC images of the

phantom. The following information was extracted from the TPS:

3. The distance between the centres of each of the individual fiducial marker

point pairs.

4. The mean distance between the all of the point pairs placed on the CT and

the PET image for both the left and right markers.

The within-series mean and standard deviation of the “distance between pairs” value

and the “mean distance” value were calculated.

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3.1.4 Results

3.1.4.1 Algorithm tests

Repeated registrations with the same pre-registration translation and rotation offsets

applied to the axes of a registered secondary image provided the same post-

registration results for all 5 registrations. This was found to be the case when using

either the CC or MI algorithm to perform the automated registrations.

Reproducibility tests of the automated registration software

The various scatter plots of the absolute values demonstrated the effect of increasing

the pre-registration translation and rotation offsets on the magnitude of the post-

registration translation and rotation parameters using either the CC or MI registration

algorithms. Appendix 2 contains the graphs for the registration results for the

different sets of images and the algorithm used to register the images. Note that

there are no graphs for the post-registration results for registering the same CT image

with itself using the CC algorithm as each pre-registration offset resulted in perfect

registration (i.e. all post-registration parameters = 0 (cm or degrees). Table 3-5

provides a summary of the algorithm tests based on the scatter plot observations and

the raw data tests as a function of algorithm, applied pre-registration offsets and the

images registered.

Cross correlation and mutual information algorithm tests

While some deviations (or outliers) could be seen throughout the results of the

algorithm tests, there were some general patterns observed across the post-

registration results. For the two algorithms these were:

• The CC algorithm resulted in:

Perfect registration where the same image was registered with

itself.

Identical registration results when the two different PET images of

the same phantom condition were registered, with very small

magnitudes of post-registration parameter values.

The same registration results except where the pre-registration

translation offsets reach a magnitude of 5 cm.

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Table 3-5 Summary of the results of the CC and MI algorithm tests Algorithm Pre-registration offsets Registered images Summary of post-registration results

CC algorithm

Applied to the translation parameters only

CT1 and CT1 Each pre-registration offset resulted in perfect registration (i.e. all post-registration parameters = 0 cm or degrees).

PET AC 1a and 1a The post-registration values = 0 (cm or degrees) except for pre-registration offsets of 5 cm and -5 cm. For these offsets, the magnitude of the post-registration values ranged from 2.31 – 10.55 cm and 0.79 – 12.90° for the translation and rotation parameters respectively.

PET AC 1a and 1b The post-registration offsets for each parameter were the same for each registration (x trans= -0.01 cm; y trans= 0.02 cm; z trans = -0.03 cm; x rot= 0.12°; y rot= -0.01°; z rot= -0.04°) except the pre-registration offsets for 5 cm and -5 cm. For these offsets, the magnitude of the post-registration values ranged from 2.73 – 9.15 cm and 2.04 – 11.44° for the translation and rotation parameters respectively.

Increasing translation and rotation pre-registration offsets applied

CT1 and CT1 The same post-registration results were observed as for those where the registration of these images was performed with translation only pre-registration offsets.

PET AC 1a and 1a The same post-registration results were observed as for those where the registration of these images was performed with translation only pre-registration offsets. For the 5 cm and -5 cm translations with increasing rotation offsets, the magnitude of the post-registration values ranged from 1.97 – 12.57 cm and 0.16 – 14.47° for the translation and rotation parameters respectively.

PET AC 1a and 1b The same post-registration results were observed as for those where the registration of these images was performed with translation only pre-registration offsets. For the 5 cm and -5 cm translations with increasing rotation offsets, the magnitude of the post-registration values ranged from 1.12 – 11.55 cm and 0.73 – 18.37° for the translation and rotation parameters respectively.

MI algorithm

Applied to the translation parameters only

CT1 and CT1 Post-registration offsets were observed for the translation parameters ranging from 0 – 0.13 cm. Each of the rotation post registration values = 0 (cm or degrees).

PET AC 1a and 1a Post registration offsets were observed ranging from 0 – 0.07 cm and 0 - 0.36° for each of the translation and rotation axes respectively.

PET AC 1a and 1b Post registration offsets were observed ranging from 0.01 – 0.27 cm and 0 - 0.58° for the translation and rotation axes respectively except the -4 cm pre-registration offsets. For this offset the magnitude of the post-registration values ranged from 0.22 – 3.39 cm and 0.34 – 22.81° for the translation and rotation axes respectively.

Increasing translation and rotation pre-registration offsets applied

CT1 and CT1 The post- registration values were observed ranging from 0 – 0.24 cm and 0 - 0.5° for the translation and rotation axes respectively, except the -4 cm, 5 cm and -5 cm translations with increasing rotation pre-registration offsets. For these offsets, the magnitude of the post-registration values ranged from 0 – 8.57 cm and 0 –89.99° for the translation and rotation axes respectively.

PET AC 1a and 1a The post- registration values were observed ranging from 0 – 0.12 cm and 0 - 0.71° for each of the translation and rotation axes respectively, except the -5 cm translation with increasing rotation pre-registration offsets. For these offsets the magnitude of the post-registration values ranged from 0 – 3.01 cm and 0.01 – 18.93° for the translation and rotation axes respectively.

PET AC 1a and 1b

The post- registration values were observed ranging from 0 – 0.26 cm and 0.02 – 1.02° for each of the translation and rotation axes respectively for the pre-registration offsets with translations less than a magnitude of 3cm. For the translation offsets equal to or greater than a magnitude of 3 cm, the post-registration values ranged from 0 – 6.63 cm and 0.01 – 28.4° for the translation and rotation axes respectively.

133

• The MI algorithm resulted in:

A magnitude of post-registration parameters less than 0.3 cm for

the translation values and, in all but one case, less than 1.0° for the

rotation parameters.

A significantly higher magnitude of post-registration parameters

for the two different PET images of the same phantom condition

were registered with pre-registration translation offsets equal to or

greater than 3 cm with rotation offsets applied.

Both algorithms demonstrated that of the 6 registration parameters, the z translation

axis and the x rotation axis had the largest magnitude of post-registration offset

values, except where the pre-registration translation offsets of higher magnitude

produced large registration errors.

3.1.4.2 Comparison of the level of accuracy and reproducibility of AC scan-

based registration versus transmission scan-based-registration

Appendix 3 contains the box plots created for the intra-PET series registrations for

both of the automated registration techniques. The registration results are relative to

the baseline registrations (where it is known that the main tank of the phantom was

correctly aligned for both the CT and the PET scans). The box plots display the

registration results as follows (see Figure 3-1):

• The 25th and 75th percentiles of the post-registration parameters as the

lower and upper lines of the box.

• The median of each of the post-registration parameters as the red line in

the middle of the box.

• The extent of the rest of the post-registration parameters is shown as

black lines extending above and below the box.

• Any post-registration parameter that is more than 1.5 times the inter-

quartile range is shown as a red plus sign.

The intra-series box plots demonstrated that the transmission scan-based registration

results were more narrowly distributed relative to the baseline registration than the

results for the AC scan-based registrations. This is demonstrated by consistently

134

lower magnitude of the inter-quartile ranges of the post-registration parameters for

the transmission-scan-based registrations. The median for each post-registration

parameter does not display any obvious level of difference between the two

registration methods, when all the intra-series registration plots are examined

together.

Figure 3-1 Box plots for the registration results for the series 2 PET images of the phantom The plot for AC scan-based registration results for the different translation and rotation registration parameters is shown in (a), while the plot for the transmission-scan-based registration results is shown in (b). (Note: n=24 registrations for each parameter).

(a)

(b)

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Figure 3-2 contains the plots of the standard deviation versus the mean of the post-

registration parameters relative to the baseline registrations for the registered images

based on the PET image used in the automated registration process. These graphs

demonstrate:

• There isn’t an apparent systematic error in the repeated registration of CT

and PET images using either the transmission or AC scan-based

registration technique (i.e. as the mean difference from the baseline

registrations increases the standard deviation does not increase).

The graph of the post-registration rotation parameters for the AC

scan-based registrations does have three points with a high mean

and standard deviation that could suggest otherwise.

• The magnitudes of the standard deviation for the different post-

registration parameters indicate that the transmission scan-based

registrations are more reproducible than the AC scan-based registrations.

Figure 3-2 The standard deviation plotted against the mean for the post registration parameters: combined AC and transmission scan-based registrations There are n=24 registrations for each parameter of the images from PET series 1-3 and 5. There are n=120 registrations for each parameter of the images from PET series 4, 6-8.

PET AC registrations

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The observations made from the plots of the standard deviation versus the mean are

confirmed by the repeatability coefficients in Table 3-6. The repeatability

coefficients provide the levels of magnitude for each registration parameter that any

two registrations will be within based on the registration results for each registration

technique. The repeatability coefficients are consistently smaller for the transmission

scan based-registration parameters compared to those for the AC scan-based

registrations.

Table 3-6 Repeatability coefficients for the different image-based automated registration techniques

Registration technique Repeatability coefficient

x trans (cm)

y trans (cm)

z trans (cm)

x rot (degrees)

y rot (degrees)

z rot (degrees)

PET AC scan-based registration 0.742 0.528 1.788 5.599 5.120 3.975

PET trans scan-based registration 0.419 0.237 0.498 0.676 1.242 0.514

Direct comparison of the means of the registration results for the two different

image-based registration techniques indicates that the transmission scan-based

technique has the higher level of accuracy, relative to the baseline registrations. The

means of the absolute values for the different post-registration parameters are smaller

for the transmission scan-based registrations compared those for the AC scan-based

registrations (see Table 3-7). The results of the paired t-tests of the means for the

different post-registration parameters (also shown in Table 3-7) demonstrate that

means are significantly different for 4 out of the 6 parameters (p < 0.05).

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Table 3-7 Paired t-test comparisons of AC and transmission scan-based mean registration results of the phantom CT and PET images

x trans (cm)

y trans (cm)

z trans (cm)

x rot (degrees)

y rot (degrees)

z rot (degrees)

PET AC scan-based registrations

mean 0.23 0.28 0.34 0.63 0.69 0.68

standard deviation 0.18 0.16 0.40 1.24 1.18 0.80

PET trans scan-based registrations

mean 0.22 0.13 0.21 0.40 0.59 0.37


Comparison of means

significant difference no yes yes yes no yes

p 0.56 <0.001 0.003 0.03 0.3 <0.001

3.1.4.3 The effects of motion on the level of accuracy and reproducibility of

automated registration techniques

Figure 3-3 contains the box plots of the combined registration results for the CT and

PET images where the components of the phantom were static. Figure 3-4 contains

the box plots of the combined registration results of the images where the sphere was

moving during acquisition. When the post-registration parameters relative to the

baseline registrations are grouped according to the absence or presence of motion in

the phantom:

• The AC scan-based registrations resulted in more widely distributed post-

registration parameters for registration of the images where the phantom

components were static compared to the registered images where the

sphere was moving.

• A decrease in the distribution of the post-registration parameters is seen

for the transmission scan-based registrations compared to the AC scan-

based registrations for both the static and moving sphere conditions of the

phantom.

While the x translation post-registration parameter for the

registrations of images with the moving sphere has a narrower

distribution of results, there is an increase in the number of

outliers (see Figure 3-4).

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Figure 3-3 Combined registration results for the CT and PET images with all components of the phantom static There are two plots for the AC-based-registrations, one with the full range of the outliers shown, the other with a y-axis range the same as for the transmission-scan-based registrations (Note: n=24 registrations for each parameter).

139

Figure 3-4 Combined registration results for the CT and PET images with the sphere moving Note: n= 120 registrations for each parameter

140

Figures 3-5 and 3-6 contain the plots of the standard deviation versus the mean of the

post-registration parameters relative to the baseline registrations for the registered

images where the components of the phantom were static or where the sphere was

moving during acquisition. These graphs demonstrate:

• There isn’t an apparent systematic error in the repeated registration of CT

and PET images for the different phantom conditions using either the

transmission or AC scan-based registration technique

The graph of the post-registration rotation parameters for the AC

scan-based registrations with static images does have three points

with a high mean and standard deviation that could suggest

otherwise (see Figure 3-5).

• The magnitudes of the standard deviation of the different post-registration

parameters indicate that the transmission scan-based registrations are

more reproducible than the AC scan-based registrations for both static

and the moving sphere images.

The graph of the post-registration translation parameters for the

transmission scan-based registrations with the moving sphere has

one outlier. One of the x translation parameters has a standard

deviation approximately 4 times larger than the other translation

parameters (see Figure 3-6).

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Figure 3-5 The standard deviation plotted against the mean for the post registration parameters – all components of phantom static The PET images are from series 1-3 and 5. There are n= 6 registrations PET scans with each static CT (represented by each point in the graphs). There are 4 series of PET images where all components were static = 4 points for each parameter).

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The repeatability coefficients in Table 3-8 demonstrate that:

• The AC scan-based registrations of the images with the moving sphere

are more reproducible than those with all the phantom components static.

• The transmission scan-based registrations have a similar level of

reproducibility for both the static and moving sphere images.

3 of the post-registration parameters (the x translation and the x

and z rotations) have smaller coefficients for the AC scan-based

registrations.

3 of the post-registration parameters (the y and z translations and

the y rotation) have smaller coefficients for the transmission scan-

based registrations.

• The transmission scan-based registrations are more reproducible than the

AC scan-based registrations for both the static and the moving sphere

images (the x translation parameter being the exception).

Table 3-8 Repeatability coefficients for the static phantom or moving sphere images

Phantom condition and registration technique

Repeatability coefficient

x trans (cm)

y trans (cm)

z trans (cm)

x rot (degrees)

y rot (degrees)

z rot (degrees)

Static components PET AC scan-based registration 0.988 0.580 2.459 7.838 7.162 5.244

Static components PET trans scan-based registration 0.148 0.226 0.636 0.313 1.657 0.492

Moving sphere PET AC scan-based registration 0.334 0.481 0.594 1.106 1.091 1.991

Moving sphere PET trans scan-based registration 0.534 0.125 0.269 0.747 0.553 0.538

Direct comparison of the means of the registration results (relative to the baseline

registrations) for the static phantom and moving sphere images demonstrates that

there is no overall difference between the levels of accuracy due to motion as:

• The means for 4 out of the 6 registration parameters are higher for the AC

scan-based registrations of the static phantom images.

143

• The means for 3 out of the 6 registration parameters are higher for the AC

scan-based registrations of the moving sphere phantom images.

• The means for 3 out of the 6 registration parameters are higher for the

transmission scan-based registrations of the moving sphere phantom

images.

The t-test results demonstrate that:

• For the AC scan-based registrations that there is no significant difference

between the means of the registered static phantom or moving sphere

images for 3 out of the 6 registration parameters.

• For the transmission scan-based registrations that there is no significant

difference between the means of the registered static phantom or moving

sphere images for 4 out of the 6 registration parameters.

Table 3-9 T-test comparisons of static or moving sphere for the AC and transmission scan-based mean registration results of the phantom CT and PET images PET AC scan-based registrations

x trans (cm)

y trans (cm)

z trans (cm)

x rot (degrees)

y rot (degrees)

z rot (degrees)

Static phantom

mean 0.21 0.15 0.80 0.98 0.82 1.32 standard deviation 0.26 0.15 0.74 2.72 2.46 1.29

Moving sphere


Comparison of means

significant difference no yes yes no no yes

p 0.48 <0.001 0.001 0.46 0.8 0.008

PET trans scan-based registrations

x trans (cm)

y trans (cm)

z trans (cm)

x rot (degrees)

y rot (degrees)

z rot (degrees)

Static phantom


Moving sphere


Comparison of means

significant difference yes no no no no yes

p 0.05 0.07 0.3 0.07 0.09 0.002

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The distance between the fiducial marker pairs corresponding to the imaged fiducial

marker positions on the various registered CT and PET images of the phantom is

shown in Table 3-10. These results demonstrate a difference in localisation of the

centre of the marker on the PET image relative to the CT image ranging from 0.09

cm to 0.69 cm. The average localisation differences are equivalent to the magnitude

of the voxel dimensions of the PET image (0.4 x 0.4 x 0.4 cm).

Table 3-10 The difference in fiducial marker localisation on registered CT and PET AC images of the phantom

Series 1 Distance between pairs Mean

dist between points


dist between points Rt marker Lt marker Rt marker Lt marker

Scan no

a 0.09 0.52 0.3

Scan no

a 0.16 0.69 0.42

b 0.11 0.3 0.2 b 0.52 0.5 0.51

c 0.19 0.27 0.23 c 0.4 0.5 0.45

d 0.64 0.58 0.61 d 0.65 0.37 0.5

e 0.44 0.33 0.39 e 0.66 0.5 0.58

f 0.09 0.32 0.2 f 0.69 0.5 0.59

mean 0.26 0.39 0.32 mean 0.51 0.51 0.51

stv 0.23 0.13 0.16 stv 0.20 0.10 0.07


dist between points



Scan no

a 0.44 0.39 0.42

Scan no

a 0.39 0.48 0.43

b 0.33 0.36 0.34 b 0.43 0.48 0.45

c 0.39 0.39 0.39 c 0.39 0.48 0.43

d 0.39 0.39 0.39 d 0.3 0.39 0.35

e 0.39 0.61 0.5 e 0.3 0.43 0.36

f 0.39 0.36 0.38 f 0.39 0.43 0.41

mean 0.39 0.42 0.40 mean 0.37 0.45 0.41

stv 0.03 0.10 0.05 stv 0.05 0.04 0.04


dist between points



Scan no

a 0.37 0.41 0.39

Scan no

a 0.64 0.5 0.57

b 0.33 0.41 0.37 b 0.33 0.43 0.38

c 0.37 0.3 0.34 c 0.33 0.5 0.42

d 0.33 0.3 0.32 d 0.31 0.42 0.37

e 0.33 0.32 0.32 e 0.31 0.42 0.37

f 0.37 0.4 0.39 f

mean 0.35 0.36 0.36 mean 0.38 0.45 0.42

stv 0.02 0.06 0.03 stv 0.14 0.04 0.09

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Direct measures of accuracy for the CC and MI algorithms were able to be achieved

by registering the same image with itself (i.e. the same CT or the same PET AC

image of the phantom). Registering two sequentially acquired PET AC images with

identical phantom conditions tested the effects of intra-subject differences on

registration accuracy.

The algorithm tests (see Table 3-5) demonstrated sub-voxel levels of accuracy for the

post-registration translation parameters and less than 1° for the rotational parameters

for both the CC and MI algorithms. The differences in the voxel sizes between the

CT and PET images did not affect the registration accuracy of the algorithms.

However accuracy was slightly worse in the z-translation and the x-rotation axes

independent of algorithm and the images registered. When the voxel dimensions of

the PET images are considered (i.e. the voxel axes dimensions are equal at 0.4 x 0.4

x 0.4 cm3 for both the AC emission and transmission images), the higher levels of

inaccuracy for z-translation and x-rotation registration parameters may be due to the

search optimisation processes of the registration software rather than image voxel

size as has been suggested94, 95

.

The z-translation and x-rotation are both relative to the superior-inferior direction of

the patient which is perpendicular to the axial (or transverse) image acquisition plane

of both the CT and PET images. The order in which the translation and rotation

parameters are optimised with respect to the 3 Cartesian axes of the images can

affect optimisation robustness88

. The software search optimisation process may

place priority on optimising the in-plane parameters (the x and y-axes translations

and the z-axis rotation as demonstrated in Figure 1-11) to find the capture range (i.e.

true registration).

While the MI algorithm is recommended for registering PET and CT images88, 89,

using the CC algorithm to register copies of the same CT and PET and two PET

images of same phantom conditions provided a baseline to assess the effects of the

registration software’s accuracy. The results for the CC algorithm registrations of

identical images were expected, demonstrating that direct greyscale matching for

146

each voxel of the images should result in perfect registration81. Accuracy was

slightly worse for the registration of the two PET AC images with identical phantom

conditions for both the CC and MI algorithms. This is mostly likely due to the

differences in voxel intensity levels resulting from decay of the 18

F-FDG activity

between the acquisition of the two images.

Initial alignment of the two images > 4 cm and > 3 cm from the capture range for the

CC and MI algorithms respectively, can result in large registration errors. Different

pre-registration image alignments resulted in different registration outcomes with

relatively small rotational pre-registration offsets affecting image registration

accuracy more than translation offsets. For example a +1 cm x-translation offset will

misalign the entire secondary image by 1 cm to the left of the primary image. A +1°

x-axis rotational offset will result in a 1.7 mm anterior displacement of the secondary

image relative the primary image 10 cm from the pivotal point of rotation of the

secondary image. Despite the smaller initial alignment offset between the two

images which a 1° rotation offset produces compared to a 1 cm translation offset, the

results of the algorithm tests demonstrated that the processes utilised by the image

registration software are more sensitive to pre-registration rotation offsets than

translation offsets.

Comparisons between AC scan-based and transmission scan-based registrations of

the phantom CT and PET images using the MI algorithm were made for two reasons.

The MI algorithm is recommended for more robust inter-modality registration86, 88

and several studies36, 90, 96

have suggested that transmission scan-based registration is

more robust than AC scan-based registration. Based on the repeatability coefficients

and comparison of the means for the different post-registration parameters, the

transmission scan-based registration technique was found to have the higher level of

accuracy and reproducibility, across multiple patient specific conditions, including

respiration. The mean and standard deviations of the post-registration parameters for

the transmission scan-based registrations relative to the baseline registrations were

0.22 ± 0.21, 0.13 ± 0.12, 0.21 ± 0.15 cm for the x, y and z translation parameters

respectively. Differences to the baseline registrations of 0.40 ± 0.43, 0.59 ± 0.44,

and 0.37 ± 0.47° were achieved for the x, y and z rotation parameters respectively.

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The AC and transmission scan-based registration parameters were compared with

baseline registration parameters as a means of evaluating the two different automatic

registration techniques. This approach has been applied in numerous image

registration analysis studies85, 86, 90, 94, 96

. The baseline registrations of the CT and

PET images were performed manually using visual assessment of the alignment of

different features of the phantom to validate the accuracy of thee registrations.

While it can be argued that these baseline registrations cannot been seen as a direct

measure of accuracy, the phantom had clearly defined regular surfaces which made

visual assessment of the registration of the CT and PET images relatively easy.

The differences in the imaged dimensions of the moving sphere between the PET and

CT images did not affect the level of accuracy or reproducibility of the transmission

scan-based registrations. The voxels representing the imaged moving sphere make

up a relatively small percentage of the total voxels in the CT and PET images of the

phantom. Therefore differences between the CT and PET images due to the moving

sphere will not significantly impact on the evaluation of the joint entropy of the two

images when they are registered using the MI algorithm. While the imaged volumes

of the moving sphere on the free-breathing CT scans varied, the mean centre of the

imaged sphere derived using the data from all 20 free breathing CT scans, was found

to be only 2 mm inferior of the true centre of the moving sphere (see Figure 2-29).

This may also be a contributing factor as to why the motion of the sphere did not

affect registration outcomes. The fact that the repeatability coefficients and

comparisons of the means showed improved registration outcomes for the AC scan-

based registrations where the sphere was moving is due to large errors for the

registration of some of the PET series 1 and 3 images. These outliers are result of

registering CT and PET images of the phantom with no water or activity in the main

tank.

The fiducial marker tests demonstrated localisation errors of the centre of the

markers on the PET images were equivalent to PET image voxel dimensions. The

imaged size of the fiducial markers on the PET images was larger than the actual

dimensions of the markers due to partial volume effects. Point-based CT-PET

registration using fiducial markers could actually introduce registration errors larger

those demonstrated using the automatic voxel-based techniques. The phantom image

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registration tests performed in this study do not support using fiducial markers as a

gold standard to assess registration outcomes as other studies have done82, 98. This

and the extra radiation exposure for nuclear medicine technologists from injecting

the 18F-FDG into the markers indicate that there is no advantage in using fiducial

markers in the CT-PET fusion process.

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3.2 Clinical trial of the image registration protocol

3.2.1 Aims

The aims of the patient image registration trials are to:

1. Perform a pilot study which will

• Combine the techniques tested in chapters 2 and 3 for image manipulation

and registration which will form the basis of the protocols to be used by

the radiation therapists in the patient data image registration trials.

• Determine baseline registration outcomes to be used to analyse the

radiation therapist image registration results.

2. Determine the level of reproducibility of the manual registration results of 12

radiation therapists registering the 9 patient planning CT and PET AC scans

acquired.

3. Determine the level of reproducibility of the automatic algorithm-based

registration results performed by 12 radiation therapists using two different

automatic registration protocols based on the phantom registration tests to

register the image data sets of the 9 patients. The automatic registration

protocols would involve:

• Registering the planning CT scan with the PET AC scan using the MI

algorithm, or

• Registering the planning CT scan with the PET transmission scan first to

use as the basis for registering the PET AC scan with the planning CT

scan.

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3.2.2 Methodology

3.2.2.1 Pilot study and RT training

A pilot study to determine baseline registration results for each patient’s CT and PET

image data sets was conducted prior to the image registration trials. Two RTs were

involved in the pilot study; the principal researcher of this project and a senior RT in

charge of a treatment planning department. Neither of these two RTs participated in

the image registration trials. The methodology used in the pilot study was as follows:

Pilot Study

1. As the planning CT and PET image data sets were acquired for each patient they

were imported into Pinnacle3

2. The data sets were then registered by one of the RTs using the Syntegra software.

The techniques tested previously for image manipulation and registration were

incorporated into the pilot study including:

.

• The window widths and levels determined from the phantom tests and

confirmed using the patient PET scans in Chapter 2.

• The pre-registration importation and alignment of images.

3. The registration results for each patient were evaluated by the other RT.

4. If there was a difference of opinion, both RTs reviewed the registration results

again and adjustments to the registration of the images were made if necessary.

5. The final registration of each patient’s CT and PET AC images provided the

baseline registrations which would be used to analyse the RT image registration

trial results.

The image registration evaluation criteria used in this study were based on matching

anatomical features that could be seen on both the CT and PET AC scans (See Figure

3-7). The criteria were as follows:

• Alignment of the vertebral bodies and sternum (Figure 3-7c).

• Alignment of air-tissue interfaces in stable regions of the patient as shown

in Figures 3-7a and 3-7b (i.e. excluding the diaphragm, and lower lobe

regions of the lungs).

151

• Alignment of any hypo or hyper-intense anatomical features of the PET

AC scan with corresponding features on the planning CT, such as the left

ventricle of the heart, the liver or kidneys (see Figures 3-7a and 3-7b).

Figure 3-7 Anatomy-based matching criteria for the baseline registrations of the patient CT and PET data sets

(a)

(b)

(c)

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Radiation therapists interested in participating in the trial were invited to an

information session which provided background information relating to the research.

Once the RTs who were going to participate in the trials had signed the participant

information and consent form, they were given a training session on PET image

interpretation and the principles of image registration. None of the participating RTs

participating in the image registration trials had any clinical experience in registering

CT and PET images or using the Syntegra software. Each participant was required to

attend a “hands-on” session to familiarise them with using the Syntegra software and

to give them a dummy run using the prescribed methodology for the trial. The RTs

were provided with the image registration evaluation criteria used in the pilot study

and asked to apply it to a patient data set they had just registered using various

visualisation tools in the Syntegra software.

RT training

3.2.2.2 RT image registration trials using patient data

The patient image data was prepared for the RTs to register in the trials. Two plans

were created for each patient, one for the manual and one for the automatic

registrations to be performed. The CT and PET image data acquired for the study

was imported for each plan. For patients 1, 3, 6 and 7 only the PET AC scan was

collected and hence imported, whereas both the PET AC and transmission scans

were able to be imported for the remaining patients (2, 4, 5, 8 and 9). The position of

the image data sets in these plans were not adjusted from their original DICOM

coordinates nor were any of the optimal window widths or levels applied for viewing

the PET.

Identical copies of the pre-registration plans were made for each RT participating in

the trial. Each RT performed the required image registrations for all 9 patients over a

2 week period, with two RTs at a time completing the trial. During the first week for

each pair of RTs, one RT was given patients 1-5 to register while the other RT was

given patients 5-9 to register. In the second week each RT was given the reverse set

of patients.

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The resources used in the training sessions were made available for the RTs to

consult at anytime during the trial. A set of instructions, detailing the methodology

to be used for the registration trials was supplied to the RTs (see Appendix 4 for a

copy of these instructions). The RTs were instructed to:

1. Use the optimal window widths and levels for viewing the PET images that had

been determined previously.

2. First register the patient images manually using the CT and PET AC scans.

3. Prior to performing the automated registrations the RTs were required to pre-

align the images so that the lungs on both the CT and the PET scans were

roughly overlaid.

4. Register the patient images automatically using the:

• CT and PET AC scans (patients 1, 3, 6 and 7 ) or

• CT, PET transmission and PET emission scans (patients 2, 4, 5, 8 and 9),

registering the planning CT scan with the PET transmission scan first,

then registering the PET AC scan with the planning CT based on the CT-

PET transmission scan registration results.

5. The RTs were not allowed to adjust the results of the automated registrations.

6. The RTs were requested to not compare their registration results with the other

RTs participating in the trial.

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3.2.3 Data analysis

The results of the RT registrations of the patient planning CT and PET images used

the same methods as those used in analysing the phantom registration tests.

The post-registration parameters for each of the RT registrations of the patient CT

and PET images were subtracted from the baseline registration of each patient’s

images.

Calculation of the post-registration results relative to the baseline registrations

Box plots of the post-registration translation and rotation parameters relative to the

baseline registrations were created for:

Box plots of the post-registration results

• The manual registration results for each patient

• The automated registration results for each patient

• The combined manual registrations for all patients

• The combined AC scan-based automated registration results (patients 1,

3, 6 and 7)

• The combined transmission scan-based automated registration results

(patients 2, 4, 5, 8 and 9)

Scatter plots of the standard deviation versus the mean difference from the baseline

registrations was created for:

Plots of the standard deviation against the mean of the registration results

• The post-registration x, y, and z translations and the post-registration x, y,

and z rotations for:

The combined manual registration results

The order that the RTs manually performed the manual

registrations in (i.e. the first and second group of patients given to

each RT to register)

• The post-registration x, y, and z translations and the post-registration x, y,

and z rotations for:

The combined automated registration results

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The combined AC scan-based automated registration results

(patients 1, 3, 6 and 7)

The combined transmission scan-based automated registration

results (patients 2, 4, 5, 8 and 9)

The order that the RTs performed the AC scan-based automated

registrations in (patients 1, 3, 6 and 7)

The order that the RTs performed the transmission scan-based

automated registrations in (patients 2, 4, 5, 8 and 9)

The repeatability coefficient (refer to Section 3.1.3.2) was calculated for each of the

6 different registration parameters. The results were grouped by the registration

technique to calculate within-subject variance to determine which of the methods

was more reproducible. The results for each registration technique were then

separated according to the order that the patients were registered by the RTs to

calculate within-variance to identify if the registration results were more

reproducible as the RTs gained experience.

Calculation of the repeatability coefficient

The RT registration results for different patients were combined. The mean and

standard deviation was calculated using the absolute values for each of the post-

registration translation and rotation parameters. A paired t-test was used to compare

the means for each of the post-registration parameters between the manual and

automated registration methods. To compare the AC scan and transmission scan-

based registration results a t-test was performed.

Comparison of the manual and automated mean registration results

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3.2.4 Results

3.2.4.1 Pilot study and RT training

Baseline registrations were achieved for each of the patient planning CT scans and

their PET AC images. It was found that there appeared to be slight differences in

patient dimensions and position between the CT and PET images. These differences

were of the order seen in daily patient positioning during radiotherapy treatment

(such as chin position as shown in Figure 3-8). During the pilot study it was found

that the lung surfaces on both data sets needed to be roughly aligned prior to running

the automatic registration software using the MI algorithm. If the surface of the

lungs were close to either the first or last slice of the CT image then misregistration

would often occur (see Figure 3-8).

Figure 3-8 Patient positioning and misregistration issues noted during the pilot study The image on the left demonstrates the slight difference in patient chin position between the CT and PET images. The image on the right demonstrates the misregistration error that could occur if the surface of the lungs were close to either the first or last slice of the CT image

Initially most of the RTs expressed concerns relating to their lack of experience in

viewing and registering PET images, feeling that this would produce poor

registration results. They all felt more confident after participating in the training

session and as they registered more of the patient data sets during the trial.

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3.2.4.2 RT image registration trials using patient data

Appendix 5 contains the box plots of the 12 RT’s results for each patient for both the

manual and the automated registration techniques. These graphs demonstrate that for

each patient there is a marked reduction in the spread of the automated RT

registration outcomes (relative to the baseline registrations) when compared to the

manual RT registration results. Generally the median for the translation post-

registration parameters are similar between the manual and automated registrations.

There are however rotation parameter differences greater than 1 cm or 1° for the

medians between the registration techniques in 6 out of the 9 patients.

Figure 3-9 contains the box plots of the combined RT registration results for each

parameter from all 9 patients for the different registration techniques. From these

graphs it can be seen that over the 9 patients that:

• The magnitude of any outliers is significantly reduced for the automated

registration techniques.

• There is a narrower distribution of the automated registration results

compared to the manual registration results.

• There is a similar distribution of results when the two automated

registrations techniques are compared.

• The medians between the manual and automated techniques are similar

for the translation parameters while more variation is seen in the rotation

parameters with approximately 0.75° difference can be seen for the y and

z rotation parameters.

158

Figure 3-9 Combined RT results of the patient CT and PET images by registration technique For the manual registrations, n=108 registrations for each parameter. For the AC scan-based registrations, n=48 registrations. For the transmission scan-based registrations, n=60 registrations.

159

Figure 3-10 contains the plots of the standard deviation versus the mean of the post-

registration parameters for the RTs results relative to the baseline registrations.

These graphs demonstrate that:

• There isn’t an apparent systematic error for either the manual or

automated registration results (i.e. as the mean difference from the

baseline registrations increases the standard deviation does not increase).

• The magnitudes of the standard deviations of the different post-

registration parameters indicate that the automated registrations were

more reproducible than the manual registrations.

Figure 3-10 The standard deviation plotted against the mean for the post registration parameters: automated and manual RT registration results There are n=12 registrations per data point.

Combined manual registrations

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Plots of the standard deviation versus the mean of the RT automated registration

results separated into AC scan-based or transmission scan-based registrations are

shown in Figure 3-11. While these graphs do not demonstrate any systematic error

for either of the automated registration techniques there appears to be no apparent

difference in reproducibility between the two techniques when the magnitude of the

standard deviations are compared.

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Figure 3-11 The standard deviation plotted against the mean for the post registration parameters: AC scan and transmission scan-based automated RT registration results There are n=12 registrations per data point.

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The repeatability coefficients calculated for the combined the manual and automated

RT registrations are markedly different (see Table 3-11) and show agreement with

the plots of the standard deviation versus the mean in Figure 3-10. On average the

automated RT registration results were 4 times more reproducible than their manual

registration results. When the automated registration results are separated into AC

scan-based or transmission scan-based registrations, the repeatability coefficients

indicate a difference of 0.1 cm or 0.1°. This demonstrates no difference in

reproducibility between the two automated registration techniques.

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Table 3-11 Repeatability coefficients for the manual and automated RT registration results

Registration technique Repeatability coefficient

x trans (cm)

y trans (cm)

z trans (cm)

x rot (degrees)

y rot (degrees)

z rot (degrees)

Combined RT manual registrations for patients 1-9

1.284 1.411 2.197 4.296 3.413 5.008

Combined RT auto registrations for patients 1-9

0.246 0.318 0.539 1.075 0.886 0.771

RT auto registrations: PET AC scan-based registration

0.187 0.221 0.703 0.901 0.752 0.671

RT auto registrations: PET trans scan-based registration

0.222 0.260 0.833 0.886 0.817 0.760

Comparison of the means of the registration results for the manual and automated

registration results demonstrate that the means of the absolute values for the different

post-registration parameters are smaller for the automated registrations compared

those for the manual registrations (see Table 3-12). Paired t-tests of the means for

the different post-registration parameters demonstrate that means are significantly

different for 5 out of the 6 parameters (p < 0.05).

Table 3-12 Paired t-test comparisons of the means of the registration parameters for the RT manual and automated registrations

x trans (cm)

y trans (cm)

z trans (cm)

x rot (degrees)

y rot (degrees)

z rot (degrees)

Manual registrations

mean 0.39 0.46 0.58 1.33 1.31 1.35


Automated registrations

mean 0.15 0.37 0.30 1.12 0.69 0.50


Comparison of means

significant difference yes yes yes no yes yes

p <0.001 0.03 <0.001 0.12 <0.001 <0.001

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Comparison of the means of the registration results for the RT AC scan-based and

transmission scan-based registrations indicates that there is no overall difference

between the levels of accuracy for these two automated registration techniques. The

means of the absolute values for the different post-registration parameters are smaller

for the AC scan-based registrations compared those for the transmission scan-based

registrations (see Table 3-13). However t-tests of the means for the different post-

registration parameters demonstrate that means are not significantly different for 4

out of the 6 parameters.

Table 3-13 t-test comparisons of the means of the registration parameters for the RT AC and transmission scan-based registrations

x trans (cm)

y trans (cm)

z trans (cm)

x rot (degrees)

y rot (degrees)

z rot (degrees)

AC-based registrations

mean 0.12 0.34 0.27 1.16 0.41 0.35


Trans-based registrations

mean 0.17 0.38 0.33 1.09 0.91 0.62


Comparison of means

significant difference no no no no yes yes

p 0.05 0.6 0.2 0.7 <0.001 0.02

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Figures 3-12 and 3-13 contain the plots of the standard deviation versus the mean by

order of registration for the manual and automated registration results. When the

manual and automated registration results of the RTs are grouped in the order that

the patient image data was supplied for registration, there is an improvement in the

reproducibility of the manual registration results in the second group of patients

registered, but not for the automated registrations. Improvement is demonstrated for

the rotation parameters only for the second group of manually registered patient

images.

Figure 3-12 The standard deviation plotted against the mean for the post registration parameters: manual RT results based on the order the registrations were performed There are n=6 registrations per point.

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Figure 3-13 The standard deviation plotted against the mean for the post registration parameters: automated RT results based on the order the registrations were performed There are n=6 registrations for each point.

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The repeatability coefficients in Table 3-14 demonstrate a reduction in coefficients

for the second group of manually registered patient images of:

• 1.5° for the x rotation parameter

• 0.6° for the y rotation parameter

• 0.8° for the z rotation parameter

Generally the repeatability coefficients are similar for the automated registration

techniques for the first and second group of registered patients. There are some

differences between the coefficients for the first and second groups of registered

patients. There are:

• A reduction of 0.5 cm for the z translation coefficient for the second

group of registered patients using the AC scan-based registration

technique.

• An increase of 0.04 cm for the x rotation coefficient for the second group

of registered patients using the AC scan-based registration technique.

• An increase of 0.04 cm for the z rotation coefficient for the second group

of registered patients using the transmission scan-based registration

technique.

Table 3-14 Repeatability coefficients based on the order that the RT performed the registrations

Registration technique and order performed

Repeatability coefficient x trans

(cm) y trans

(cm) z trans (cm)

x rot (degrees)

y rot (degrees)

z rot (degrees)

1st1.362 group of patients given to RTs

to manually register 1.443 2.230 5.059 3.743 5.452

2nd1.304 group of patients given to RTs

to manually register 1.350 2.188 3.541 3.152 4.657

1st

0.207 group of patients given to RTs

to automatically register: PET AC scan-based registration

0.163 0.926 0.666 0.747 0.780

2nd


to automatically register: PET AC scan-based registration

0.256 0.449 1.026 0.766 0.567

1st


to automatically register: PET trans scan-based registration

0.301 0.337 1.227 0.933 0.612

2nd

0.340 group of patients to RTs to

automatically register: PET trans scan-based registration

0.434 0.363 1.141 0.920 1.026

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The clinical trial of the image registration protocols based on outcomes of the

phantom tests, demonstrated that the automatic registration of the patient CT and

PET images were more accurate and reproducible than the manual registrations. The

mean and standard deviations of the post-registration parameters for the automatic

registrations relative to the baseline registrations were 0.15 ± 0.11, 0.37 ± 0.39, 0.30

± 0.22 cm for the x, y and z translation parameters respectively. Differences to the

baseline registrations of 1.12 ± 0.89, 0.69 ± 0.69, and 0.50 ± 0.58° were achieved for

the x, y and z rotation parameters respectively.

Unlike the phantom registration results, there was no significant difference in the

reproducibility or the level of accuracy of the AC scan and the transmission scan-

based registrations of the patient images. This may be the result of differences

between the simulated phantom conditions and patient physiological changes. It was

noted that there were large registration errors for some of the phantom AC scan-

based registrations where the main tank of the phantom was empty. These outliers

would have increased the mean post-registration parameters relative to the baseline

registration parameters. The effects of daily patient changes in dimensions and

respiration effects on the whole chest region may result in differences between the

dimensions of the AC and transmission scans which would not have occurred in the

images of the main tank of the phantom.

While the manual RT registrations of the patient images were less accurate and

reproducible than their automatic registration results, when the manual registration

results were grouped in the order the registration were performed by the RTs, their

results improved. This is most likely due to the RTs gaining more experience

assessing the alignment of different anatomical features on the patient CT and PET

images. The clinical experience of the RTs who participated in the trials range from

1 – 20 years, however none of them had previous experience with registering PET

images or using the Syntegra software. As there was no improvement in the

automatic registration results with experience, the training session prior to

participation in the image registration trials most likely reduced bias in the

registration results due to lack of familiarity with the software. Rousson et al’s121

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review of methods for assessing intra and inter-observer, and test-retest

reproducibility, highlights the importance of training to achieve higher levels of

reliability for different techniques.

As the phantom demonstrated that initial alignment of the two images should within

3 cm from the capture range (i.e. true registration) for the MI algorithm to avoid

large registration errors, the RTs were instructed to roughly align the images prior to

executing the automatic registration software. The pilot study for the image

registration trials also highlighted the importance of reasonably aligning the images

prior to registration due to the truncation of the planning CT scans of the patient

producing registrations errors86. The top and bottom slices of the planning CT scans

can be interpreted as local minima which can be seen as correctly aligned with the

surface of the patient’s lungs on the PET image by the MI algorithm81

.

Assessment of the RT registrations of the patient images used the same approach as

that used for assessing the automatic registrations of the phantom images. Baseline

registrations were achieved via manual alignment of the patient CT and PET images

using visual assessment of the alignment of different patient features to validate the

registration. Again, it can be argued that these baseline registrations cannot be seen

as a direct measure of accuracy. The use of experienced RTs to visually verify the

baseline registrations used to assess automated registration outcomes of patient

images is supported in the literature90, 94, 98

.

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4

4.1 Phantom contouring tests

GTV delineation technique evaluation and protocol

development for the clinical trial

4.1.1 Aims

To investigate if a threshold contouring technique for PET images can be applied in

the radiotherapy treatment planning environment. Various threshold values applied

to a semi-automated contouring technique will be used to generate 3D contours of

regions of interest on registered CT and PET AC images of the phantom CT and

PET. The resulting contours will be assessed to determine threshold values that

define geometrically accurate regions of interest. The effect of the following PET

image characteristics on selecting appropriate threshold values will be examined:

1. The effect of different concentrations of 18-FDG uptake in a region of interest.

2. The effect of increasing background activity relative to a region of interest.

3. The effect of a region of interest under the influence of motion.

4. Using pixel versus SUV data.

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4.1.2 Methodology

4.1.2.1 Threshold value contouring of phantom PET AC data

The baseline registered pairs of CT and PET AC images of the phantom (see Table

3.4 in Chapter 3), were used to perform the threshold contouring tests. On each of

the PET AC scans used in this series of registered images, the central rods of the

main tank and the static or moving sphere were contoured using various threshold

values. The results of the image quantification of the phantom PET scans in Chapter

2 provided the maximum pixel and SUV data for each of the features of the phantom,

from which different threshold values (percentages of the maximum value) were

calculated.

The technique that was used to determine each contour was as follows (using the

PET AC scan 4a of the phantom as an example):

1. A given percentage of this maximum value was calculated based on the

maximum pixel value of 2414 for the 0.5 cm central rod in this image. For

example the 20% threshold value for the 0.5 cm rod on this AC image was 0.2 x

2414 = 483

2. A semi-automated contouring technique that edge detects voxels within a

specified range of on a specified image data set was used. This involves:

• Creating a new contour and associating it with the PET AC image data

set.

• Specifying a minimum (483) and maximum (kept as the maximum pixel

number of the image) voxel pixel value range that that is to be included in

the contour (see Figure 4-1).

• The auto-contour tool that performs edge detection throughout all the

slices of the image data set was selected and the curser was placed on the

image to indicate where the edge detection was to begin.

3. A range of threshold values were used to create a series of contours of the 0.5 cm

diameter rod on the PET scan using the semi-automated contouring technique.

4. The volume of each contour generated from the different threshold values was

recorded.

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5. The minimum SUV value of each contour was determined using the ROI

statistics tools in the TPS and recorded.

Figure 4-1 Creation of the 20% threshold contour for the 0.5 cm rod using the semi-automated contouring technique

4.1.2.2 Verification of the geometrical accuracy of the threshold values

For each of the central rod and static sphere contours, the following technique was

used to determine which of the threshold values resulted in a contour that accurately

defined the geometric edge of the different components of the phantom on each PET

AC scan:

1. The CT scan that each PET AC scan had been registered with was used as a

visual template. Each CT scan was displayed with a mediastinal window width

and level so that the water inside each compartment could be differentiated from

the Perspex.

2. The PET AC was displayed with the window width and level that best

demonstrated regions of intense uptake.

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3. Rod and static sphere contour verification:

• The contour which followed the inside of each rod and the static sphere

on the CT scan and conformed to the shape of the corresponding region of

intense uptake on the PET AC scan was identified (see Figures 4-2a and

4-2b).

4. Moving sphere contour verification:

• The 4D volume contour of the moving sphere was used as the template

instead of the imaged CT data. The 3D model of inner perspex

dimensions of the moving sphere was positioned on each CT scan at the

known central coordinates of the static sphere position relative to the

main tank.

• The contour which followed the shape of the 3D model of the inner

dimensions of the moving sphere and conformed to the shape of the

corresponding region of intense uptake on the PET AC scan was

identified (see Figure 4-2c).

5. An important selection criterion for any contour was that no part of the line of the

contour went inside the inner edge of perspex that defined each compartment (as

visualised on each CT scan).

6. The threshold value identified as resulting in a geometrically accurately contour

was recorded.

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Figure 4-2 Verification of the geometrical accuracy of the threshold values Visual verification using the CT images of the phantom to determine the geometrical accuracy of the contours generated using different threshold values for (a) the central rods and (b) the static sphere. The 4D volume contour of the moving sphere was used to verify the accuracy of the contours for the moving sphere as shown in (c)

(a)

(b)

(c)

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4.1.3 Data analysis

There were 6 PET AC images for each phantom condition and each of these images

was contoured using a range of threshold values. For each AC image, the threshold

value that was observed to be a geometrical match for the various contoured

components of the phantom was noted. The threshold value which most frequently

resulted in a visual match with the CT image of the phantom was considered to be

the most appropriate value to produce an accurate contour for a particular phantom

component for each PET series of AC images.

Verification of the geometrical accuracy of the threshold values

The most frequently verified threshold value for each central rod (grouped by the

background-activity ratios of the main tank to the central rod or sphere) was plotted

against central rod diameter to determine any relationship between threshold values

and background activity.

The mean volume of each verified threshold value was calculated for each

component of the phantom for each of the PET series using the contour volume data

of the 6 AC images within each series. This mean volume was compared to the

baseline CT and PET 3D MBS generated contours of the phantom performed in

Chapter 2.

Comparison of pixel-based and SUV-based threshold values

The minimum and maximum SUV values contained within each threshold-generated

contour were used to determine the percentage maximum threshold value for the

SUV data. The resultant SUV-based percentage maximum threshold values were

compared to the pixel-based values used to generate the contour.

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4.1.4 Results

4.1.4.1 Threshold value contouring of phantom PET AC data

The threshold values that resulted in accurate definition of each phantom component

for the different PET scan series are shown in Table 4-1.

Table 4-1 Threshold values verified for GTV definition by visual match with CT

Phantom component PET scan series and their % background activity in main tank

1 (0%)

2 (0%)

3 (0%)

4 (0%)

5 (5%)

6 (10%)

7 (20%)

8 (40%)

0.5cm rod 30 30 30 30 55 70 - - 1.0cm rod 25 25 30 30 30 40 60 - 1.5cm rod 25 25 30 30 30 35 45 - 2.0cm rod 25 25 30 30 30 30 40 55 3.0cm rod 30 25 30 30 30 30 40 50

Sphere (diameter = 4cm) Sphere is surrounded by air (0% background)

Static sphere - - 30 - - - - - Moving sphere - - - 10 - 10 10 10

Each of the central rods was injected with different concentrations of 18F-FDG for

the series 2 phantom scans. For the series 1, 3 and 4 scans, the same concentration of 18

F-FDG was injected into the central rods. A comparison of the verified threshold

values for the contours of the central rods from series 2 with those verified for series

1, 3 and 4, indicates that different concentrations of activity in the central rods does

not significantly effect the threshold values to produce a geometrically accurate

contour.

The scans in series 1-4 were all acquired without any background activity in the main

tank of the phantom. The 30% threshold value most frequently produced accurate

contours for the central rods in the absence of background activity in the main tank.

It can be seen from Table 4-1 that as the background-activity ratios of the main tank

to the central rod or sphere increased (series 5 – 8), the threshold values which

produced a geometrically accurate contour also increased.

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The 0.5 cm diameter central was not able to be accurately contoured using any

threshold value for when the main tank background activity was 20%. Only the 2.0

cm and 3.0 cm diameter central rods were able to be accurately contoured when the

main tank background activity was 40%. The graph in Figure 4-3 demonstrates the

relationship between the central rod diameter and the threshold value required for

accurate contouring as the main tank background-activity ratio increased.

Figure 4-3 Threshold values plotted against the volume of interest (central rod) diameter

0

10

20

30

40

50

60

70

80

90

100

0 0.5 1 1.5 2 2.5 3 3.5

VOI diameter (cm)

Thre

shol

d va

lue

(% V

OI m

ax)

0% background

5% background

10% background

20% background

40% background

It was also found that motion affected the threshold levels for the sphere. A 30%

threshold value accurately contoured the static sphere in the series 3 images. For the

images where the sphere was moving (series 4, 6, 7 and 8), the threshold value that

accurately contoured the 4D volume of the moving sphere was reduced to 10% (see

Table 4-1).

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The mean volumes for the phantom components relating to the verified threshold

levels were less than the baseline CT and PET contours for all of the phantom

components except for the 0.5 cm rod (see Table 4-2).

Table 4-2 Mean volumes of the baseline contours of the phantom compared to those generated using the verified accurate threshold values (BL = baseline CT contour volumes and the intra-series PET AC mean volumes from in Chapter 2. TH = verified threshold value intra-series mean volumes)

Images 0.5cm 1.0cm 1.5cm 2.0cm 3.0cm Static Moving

BL TH BL TH BL TH BL TH BL TH BL TH BL TH

CT 4.7 - 15.7 - 33.8 - 57.1 - 126.7 - 20.3 - 40.5 -

PET Series 1 7.3 7.0 16.6 14.0 34.5 24.0 61.4 45.0 133.6 99.5 - - - -

PET Series 2 7.7 6.9 21.9 11.2 40.9 17.1 62.9 43.6 128.6 102.1 - - - -

PET Series 3 7.2 7.6 16.5 12.5 34.8 18.1 61 40.7 136.2 96.8 21.0 19.4 - -

PET Series 4 6 10.1 22.5 10.5 40.2 20.2 62.7 35.8 129.7 76.9 - - 45.5 36.8

PET Series 5 8.5 10.9 14.4 23.4 35.6 36.9 61.7 42.4 138.3 121.5 - - - -

PET Series 6 5.8 8.6 21.8 18.5 42.4 32.2 62.2 50.1 136.9 126.7 - - 45.1 28.2

PET Series 7 7.5 - 16.5 8.2 37.4 24.7 61.4 40.4 138.8 99.0 - - 45.3 23.8

PET Series 8 7.8 - 16.1 - 34.6 - 62 53.5 133.5 74.4 - - 45.4 23.1

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The ratio of the minimum SUV value of a given component contour to the maximum

SUV of the component demonstrated that equivalent contouring results would be

achieved using the same percentage maximum threshold value based on the SUV

data as the pixel-based percentage maximum threshold value used to generate the

contours. Table 4-3 contains a sample of these results for the 1.5 cm central rod for

the PET AC image data from the series 4 scans of the phantom.

Comparison of pixel-based and SUV-based threshold values

Table 4-3 Comparison of the percentage maximum threshold value for the SUV data based on the contouring results The data in the volume of interest (VOI) column shows the maximum pixel and SUV values for the 1.5 cm central rod for the PET AC image data from the series 4 scans of the phantom. The minimum pixel values are those calculated to contour the 1.5 cm rod for a particular percentage maximum threshold value (i.e. 20% and 30%). After the contour was generated using the pixel-based threshold, the minimum SUV data was extracted from the contoured volume. The ratio of the minimum SUV value to the maximum SUV value provides the equivalent percentage maximum threshold value.

Scan no

VOI image data 20% threshold based contours 30% threshold based contours

max pixel value

max SUV

Min pixel value

used to generate

the contour

Contour min SUV

Equiv % max SUV

Min pixel value

used to generate

the contour

Contour min SUV

Equiv % max SUV

a 11770 62.9 2354 12.6 20.0% 3531 18.9 30.0%

b 9054 52.1 1810.8 10.5 20.2% 2716.2 15.9 30.5%

c 23333 144.9 4666.6 29.0 20.0% 6999.9 41.8 28.8%

d 20696 138.4 41392 27.7 20.0% 6208.8 41.6 30.1%

e 18683 135.4 3736.6 27.1 20.0% 5604.9 40.7 30.1%

f 6345 49.5 1269 9.9 20.0% 1903.5 14.9 30.1%

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A semi-automated adaptive threshold technique was found to accurately contour the

geometric edge of the different features on the phantom PET images. This

contouring technique used threshold values based the maximum pixel value

measured within a VOI. The value of the threshold level which provides an accurate

contour of a VOI is dependant on object diameter and the background-activity ratio

of the surrounding voxels. However a 30% threshold level could be applied to

objects independent of diameter size when there was no background activity.

Comparison of pixel-based and SUV-based threshold values demonstrated that

identical contours would be created on the PET image if the same threshold values

were applied to the maximum SUV values measured within a VOI (see Table 4-3).

The plotted results of threshold variation with respect to VOI diameter and

background-activity in Figure 4-3 correlate with the plotted results of Erdi et al’s105

study which also tested an adaptive threshold contouring technique using PET

images of a phantom. Erdi et al’s105 study found that a 40% threshold value

accurately contoured objects larger than 4ml with background-activity ratios less

than 20%. Similar phantom studies by Yaremko et al122 and Okubo et al123

found

that thresholds of 35% and 40% respectively accurately contoured spheres with a

diameter > 2.0 cm.

The qualitative assessment of the PET images did not appear to image the moving

sphere over its full range of motion when the activity in the main tank relative to the

central rods and sphere increased above 20%. However, the contouring tests results

demonstrated that an accurate 4D volume of the moving sphere could be contoured

on the PET images, regardless of background activity. The threshold level which

produced an accurate 4D volume of the moving sphere was 10%. Other phantom

studies which imaged a moving sphere on a stand alone PET scanner also concluded

that an accurate 4D volume could be determined from PET images and also observed

a reduction in contouring threshold values for the moving sphere compared to static

objects70, 122. Based on the results of this study (which are supported by the results of

Caldwell et al70 and Yaremko et al122) it can be concluded that ITV margins for

moving objects are included in the contoured PET volumes using images acquired on

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stand alone PET scanners. This has implications for the margins applied to GTVs to

derive a PTV for lung tumours. Uncertainty in localisation of the GTV dimensions

due to respiration can now be accounted for when 10% of the maximum uptake

within a tumour is used to contour a GTV on a whole-body 18

F-FDG PET image.

Okubo et al123 obtained all the images for their phantom study on a combined

CT/PET scanner and found that the imaged range of motion for a moving sphere was

smaller on the PET images than the actual sphere’s range of motion. This is due to

the faster acquisition times for the CT-based attenuation correction of the PET

emission scans on a CT/PET scanner compared to those than the transmission scans

obtained on a stand alone PET scanner124. 4D CT and PET images are required to

accurately image moving objects imaged on combined CT/PET scanners125, 126 which

Okubo et al’s123 study did not perform when imaging the moving sphere on their

phantom. From Okubo et al’s123

results it can be concluded that ITV margins are not

included in the contoured PET volumes using images acquired on combined CT/PET

scanners which have not been co-ordinated with a patient’s respiratory cycle.

Repeating the phantom imaging and contouring tests on a combined CT/PET scanner

with respiratory gated imaging protocols is recommended for future research.

The threshold values were deliberately chosen in this study so that the resulting

contours encompassed all the voxels on the PET images which visually corresponded

to the same feature on the transverse images of the registered CT scan. Okubo et

al123

commented that their 35% threshold value produced contours which were 3 mm

smaller than the actual sphere diameter in the transverse plane. It was felt that the

criteria for determining accurate contouring threshold values used in this study would

avoid under-estimates of the dimensions of contoured PET volumes. Table 4-3

indicates that the threshold method for contouring resulted in volumes systematically

lower than the baseline volumes. Closer inspection of the threshold contouring

results demonstrated that the most superior and inferior aspects of each of the

phantom components were not contoured, despite accurate contouring in the

transverse planes. This may be due to the loss of counts at the superior and inferior

edges of the phantom.

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Despite these phantom related contouring errors, two reviews of CT/PET contouring

techniques in radiotherapy (MacManus et al127 and Nestle et al128) indicate that

adaptive thresholding techniques have demonstrated the highest levels of accuracy to

date. It was also reported by MacManus et al127 that this contouring technique has

also been found to correlate to with pathological specimens of tumour volumes. This

provides independent support for the adoption of the adaptive thresholding technique

tested and verified on the phantom images into a clinical protocol.

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4.2 Clinical trial of the GTV delineation protocol

4.2.1 Aims

The aims of the patient GTV contouring trials are to:

1. Perform a pilot study which will

• Incorporate the results of the phantom contouring tests to form the basis

of the protocols to be used by the radiation oncologists in the patient GTV

contouring trials.

• Determine the threshold values that should be used for contouring the

GTVs on the PET AC scans of each patient.

2. Train the participating radiation oncologists to use:

• The image manipulation and contouring tools available with the image

registration software in the treatment planning system.

• The protocols and instructions for the trial.

3. Compare the outcomes of the CT only and the PET AC defined GTV results to

determine the level of reproducibility of each of the different contouring

methods.

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4.2.2 Methodology

4.2.2.1 Pilot study and RO training

4.2.2.1.1 Pilot study

The baseline registrations of the patient CT and PET images image registration trials

were used to perform a pilot study to determine the threshold values for contouring

the GTV on the PET AC data of each patient. This was done to keep the time

required to complete the contouring process in the trials to a minimum. The

methodology for the pilot study was:

1. The following window width and levels were used to view the CT and PET

images:

• CT

o The TPS system defined Lung preset was used for viewing for

volumes in lung region or

o The TPS system defined Thorax preset for volumes and involved

nodes in the chest wall or mediastinum.

• PET (from the results of the window width and level tests in Chapter 2)

o Window = 0.2 and level = 0 for viewing of background and intense

uptake.

o Window = 0.2 and level = 0.1 for viewing intense uptake regions only

2. The maximum diameter in the transverse plane was measured for each VOI that

corresponded to the intense uptake regions identified as malignant lung cancer on

each patient’s PET AC scan (based on the radiologist’s report).

3. The same semi-automated contouring technique used for the phantom contour

tests was used to contour the GTV (primary and lymph nodes) of each patient

based on:

• The results of the image analysis of the patient PET AC images in

Chapter 2 to get the background percentage of uptake relative to the

maximum count of each of the VOIs

• The results of the phantom contouring tests for selecting an appropriate

threshold value for defining the geometric edge of a GTV on the PET AC

data.

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4. The window width and level used for viewing intense uptake regions only on the

PET AC scans was applied post contouring to verify that the threshold level was

appropriate.

• If the final contour was much larger than the intense region of uptake then

the threshold value need to be increased

• If the contour was smaller than the intense region of uptake then the

threshold value needed to be decreased.

The MBS tools were not available on the clinical systems during the period that the

ROs would be participating in the GTV delineation trials. Hence, a technique for

determining the maximum pixel value in intense uptake regions without using the

MBS model of a sphere needed to be determined for the trials. The following

technique was tested to see if it provided equivalent maximum pixel value results:

1. Viewing the PET AC image only, the transverse slice corresponding to the

middle of the VOI (identified as the GTV or involved lymph nodes) was located

by scrolling through the images.

• The middle of the VOI was half way between the most superior and

inferior slices the VOI was imaged on.

2. A profile of the pixel values was taken through the VOI using the Pinnacle3

3. The maximum pixel value on the line profile was used to calculate the minimum

count value using the appropriate threshold level for that patient so that the semi-

automated contouring technique could be applied.

tools

and drawing a line through the VOI on the identified middle slice (see Figure 4-

4)

4.2.2.1.2 RO training

Radiation oncologists interested in participating in the GTV delineation trial were

invited to an information session which provided background information relating to

the research. As with the RTs the ROs who were going to participate in the trials had

to sign the participant information and consent form. To familiarise the ROs with the

semi-automated threshold technique for GTV definition on PET data, the principal

researcher was available during the contouring of the first patient’s GTVs.

184

Figure 4-4 A profile of the pixel values taken through the identified GTV on a PET AC image The graph of the profile through the VOI is giving the units for the PET AC image as CT numbers. This is unit is displayed for all images (CT, MRI, PET or SPECT) but the tool is providing the actual image pixel.

4.2.2.2 RO contouring trial using patient data

The patient image data was prepared for the ROs to contour in the trials. Two plans

were created for each patient, one with the CT scan only and the other with pre-

registered CT and PET AC scans. The CT and PET scans were registered using the

translations and rotations from the baseline registrations results for each patient

image data sets in the registration trials. Separate copies of the two plans created for

each patient were made for each RO so as to avoid any comparison or biasing of

their individual results if they all contoured on the same plan.

185

The participating ROs were provided with a number of resources to use including:

• The patient’s clinical notes and PET report to assist them with locating

the malignant disease on the images.

• Reference information on PET image reconstruction and interpretation.

• Instructions from the Pinnacle3

• A set of instructions (see Appendix 6), detailing the methodology to be

used for the GTV delineation trials was supplied to the ROs.

user manual for the Syntegra program.

The ROs were instructed to perform the following steps for each patient (see

Appendix 6 for the specific use of the Syntegra software for the contouring):

1. On the plan with the CT image only

• Adjust viewing window widths and levels depending on the location of

the primary (Lung or Thorax preset)

• Outline the primary separately from any involved nodes and label each

discrete volume of disease as GTV1, GTV2 etc.

2. On the plan with the registered CT/PET data

• Adjust the window widths and levels as required:

o CT (Lung or Thorax preset).

o PET (either to view background and intense uptake or intense uptake

regions only).

• Locate the middle of each region of intense uptake corresponding to

malignant disease on the PET AC scan.

• Determine the maximum pixel value of each VOI using a line profile

through its mid-region.

• Use the semi automated contouring technique with the threshold values

(ranging from 20% - 30%), to contour the primary and any involved

nodes (labelling each contour as previously instructed for the CT only

GTVs).

• The ROs were allowed to edit the results of the semi-automated

contouring technique.

3. The ROs were requested to not compare their registration results with the other

ROs participating in the trial.

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4.2.3 Data analysis

The mean and standard deviation of the RO contours on the CT image only and the

registered CT and PET AC images were calculated.

RO contouring trial using patient data

The percentage difference in the combined RO contours for each patient was

calculated for both the contours performed using the CT image only and those

performed using the registered CT and PET AC images and the contouring protocol

provided. The percentage difference of the combined contours was determined by:

1. Adding each of the RO contours together for each patient for the CT only

contoured GTVs.

2. Subtracting each individuals RO’s contours from the combined contour to obtain

a contour that represented the difference of each RO’s contour from the

combined contour.

3. Each of the contours representing the difference of each RO’s contour from the

combined contour were combined to create a contour which represented the

voxels of the CT image which were not mutually contoured by all the ROs.

4. The volume of the contour which represented the voxels of the CT image which

were not mutually contoured by all the ROs divided by the volume of the contour

which combined each of the RO’s contours, resulting in the percentage difference

of the combined RO contours.

5. Steps 1 -5 were repeated for the RO contours performed using the registered CT

and PET AC images and the contouring protocol.

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4.2.4 Results

4.2.4.1 Pilot study and RO training

The results from the pilot study used to determine the threshold values for GTV

definition on the patient PET AC images are shown in Table 4-4. It was found that a

threshold value range of 20% to 30% was appropriate for contouring the identified

primary tumour and involved lymph nodes for patients 2 – 9. A 20% threshold value

was required where the primary tumour was mostly or completely surrounded by

lung tissue. A threshold value range of 25% - 30% was required where the primary

tumour and the involved lymph nodes were surrounded mostly by non-lung tissue

(i.e. the mediastinum or the chest wall). These threshold values were visually

correlated on the patient CT images using the PET viewing window protocols

developed in Chapter 2.

Table 4-4 Results of the pilot study used to determine the threshold values for contouring the GTV on the patient PET AC images

Patient Tissue

surrounding the GTV VOI

Background-activity ratios to be used for contouring max VOI

diameter (cm)

Selected contouring threshold

value Liver or

lung-based Background-activity ratio

1 non-lung liver-based 76.6 nodes = 1.0 excluded from study

2 non-lung and lung liver and lung-based

21.8 (liver) 9.3 (lung) primary = 2.5 25

3 predominately non-lung liver-based 27.5 primary = 6.5 30


23.5 (liver) 10.3 (lung)

nodes = 1.5 primary = 5.5

30 (nodes) 25 (primary)

5 predominately non-lung tissue liver-based 19.4 primary = 8.5 30

6 predominately lung tissue lung-based 5.1 primary = 6.5 20


27.5 (liver) 10.7 (lung)

nodes = 1.5 primary = 4.5

30 (nodes) 20 (primary)

8 lung tissue lung-based 9.7 primary = 2.5 20

9 predominately non-lung tissue liver-based 4.8 primary = 10.5 30

It was decided to exclude patient 1 from the RO contouring trial during the pilot

study. This patient had undergone a right pneumonectomy 2 years prior to their

188

current presentation for treatment with lymph node recurrence in the right upper

chest wall and mediastinum. A combination of altered anatomical structure position

and the high background-activity ratio relative to the involved lymph nodes would

add to the complexity of defining contours using this patient’s PET AC image.

It was found that use of the profile tool to determine the maximum pixel value in the

VOI to be contoured, was a suitable alternative to using the MBS tools. The profile

tool would not be as precise as determining the maximum using the MBS model of a

sphere converted to a contour encompassing the VOI on the PET AC image. For

example the profile results shown in Figure 4-4 are for patient 3. The maximum

pixel value would be estimated at 8000, with a 20% threshold value equalling 1600.

The maximum pixel in the VOI to be contoured is 8022 based on the data extracted

from the contour generated from the MBS model of a sphere. The 20% threshold

value based on this data would be 1604.

4.2.4.2 RO contouring trial using patient data

One of the 6 ROs withdrew from the trial due to work commitments, so only 5 ROs

completed the contouring trial. Images of the contouring results for the 5 different

ROs are shown in Figures 4-5 to 4-7. The contours using the CT only and the

registered CT and PET AC images for each RO are overlaid on each image.

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Figure 4-5 RO contouring results: Patients 2 – 4

Patient 2 CT only Patient 2 CT/PET



190

Figure 4-6 RO contouring results: Patients 5 – 7




191

Figure 4-7 RO contouring results: Patients 8 and 9



192

The mean and standard deviation of the RO contours on the CT image only and the

registered CT and PET AC images for each patient are shown in Table 4-5. The

percentage difference values (see Table 4-6) indicate the percentage of the total

voxels included in the combined RO contours which were not commonly contoured

by all the ROs.

Table 4-5 The mean volumes of the RO contours

Contours Patient

2 3 4 5 6 7 8 9

CT image only

Mean volume (cm3 113.8 ) 125.6 100.7 144.3 208.4 27.5 2.3 494.2

Standard deviation (%) 48 23 93 47.9 16.2 21.5 21.8 10.5

CT/PET images

Mean volume (cm3 20.3 ) 124.0 83.0 255.3 203.6 45.9 5.0 426.6

Standard deviation (%) 17 31 28 12 15.6 33 42 15

Table 4-6 The percentage differences in the volumes of the RO contours

Contours Patient

2 3 4 5 6 7 8 9

CT image only

Combined volume (cm3 209.5 ) 191.7 275.4 314.9 298.0 46.7 3.6 656.4

Combined difference (cm3 182.4 ) 108.3 239.4 250.7 137.6 31.8 1.7 258.8

% combined difference 87.1 56.5 86.9 79.6 46.2 68.2 48.0 39.4

CT/PET images

Combined volume (cm3 28.6 ) 176.4 147.1 314.3 248.2 90.2 8.3 534.2

Combined difference (cm3 10.0 ) 89.9 82.3 74.2 69.7 61.6 4.6 138.1

% combined difference 35.0 51.0 56.0 23.6 28.1 68.3 55.9 25.8

Images of the percentage differences between 5 different RO’s contours are shown in

Figures 4-8 to 4-10. The blue contours demonstrate the voxels not commonly

contoured by all 5 ROs when the contours were defined using the CT image only.

The yellow contours demonstrate the voxels not commonly contoured by all of the

ROs on the registered CT and PET AC images.

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194





195

Figure 4-10 The percentage differences for the combined RO contours: Patients 8 and 9



196


The pilot study was necessary as the effects of particular patient specific conditions

on the selection of contour threshold values could not be tested on the phantom

images. The uptake in the central rods and the sphere were homogenous while

heterogeneous uptake can occur within tumours (this effect was clearly demonstrated

in patient 3’s PET image). The moving sphere was surrounded by air on the

phantom and that none of the tumours in the patient images were surrounded by

tissues with 0% background activities (an issue also highlighted when the viewing

window protocols determined on the phantom images were applied to the patient

images).

It was found that a threshold values ranging from 20 – 30% were suitable for

contouring the 8 patient images which were to be included in the RO contouring

trials. The tumour dimensions and background activity ratios determined on each

patient’s PET image (see Table 4-4) were used to interpolate the phantom contouring

results to factor in the effects of motion. These threshold values were visually

correlated on the patient CT images using the PET viewing window protocols

developed in Chapter 2.

A 20% threshold value was suitable for contouring tumour volumes not fixed to the

chest wall or mediastinum (i.e. more heavily influenced by respiration with larger

ranges of motion67

), which was higher than the 10% threshold value which correctly

contoured the moving sphere in air on the phantom. This increase in threshold value

for moving volumes surrounded by tissues with background activity does follow the

pattern observed in phantom tests; that contouring threshold values will increase as

background activity increases. The 30% threshold was suitable for the smaller nodes

and primaries affected by respiration and the larger tumours which were less heavily

influenced by respiration. This range of threshold values (based on the maximum

pixel value measured within a VOI) did not under-estimate the dimensions of

tumours with heterogeneous uptake (see Figure 4-5). The maximum pixel value was

selected for the clinical protocol trial as the SUV data was not available for each

patient’s PET AC image.

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The clinical trial of the contouring protocol found that consistent primary tumour

volumes across different oncologists could be obtained using the registered CT/PET

images, compared to those contoured using the planning CT scans only. However

the trial also demonstrated that variations in the contouring of regional lymph nodes

were not eliminated using the CT/PET fusion contouring protocol. The largest

reduction in the percentage differences for the combined RO contours for the PET

based contours were seen for patients 2 and 5, where the boundaries of the tumour

were not clearly defined on the CT images. Even when the patients had well defined

primary volumes on the CT images, the percentage differences were less for the

CT/PET contours than the CT only contours. Patient 8 is the exception to this with

the percentage difference of the combined CT-only contours = 48.0% while the

CT/PET contours = 55.9%. The increase in the percentage difference for this patient

may be a product of the PET image resolution and the small dimensions of the

tumour. For the patients with mediastinal lymph node involvement there was large

variation in the delineation of these nodes. This is most likely due to differences in

opinion between the ROs as to which lymph nodes to include particularly when

uptake in the nodes was not clearly indicative of malignancy.

The ROs were allowed to edit their contours in the trial for two reasons. The semi-

automated technique can contour individual voxels outside of the GTV with

intensities similar to the threshold intensity depending on where the cursor is placed

as the start point for the automatic generation of the contour. Close proximity of the

tumour to normal tissues which have higher levels of uptake can result in the normal

tissues being included in the GTV volume. Similar problems are encountered when

thresholds and the automatic contouring tools are used to contour normal tissues and

structures on planning CT scans.

The semi-automated adaptive threshold contouring technique and the viewing

window protocols to the patient images was successfully applied by the participating

ROs in the GTV definition trial. Feedback from the ROs indicated that initially they

found the technique a bit laborious especially using the profile tool to determine the

VOI maximum (instead of using the MBS tools to load a spherical model over the

primary and involved nodes). Some of the ROs commented that once they became

more familiar with the technique they found that it cut down on their contouring time

198

because they were not manually contouring the GTVs slice by slice on the registered

CT and PET images.

While the RO contouring trial can only be considered a proof of concept study due to

the small number of patients, it clearly demonstrated that the use of CT/PET fusion

contouring protocol can reduce inter-user variation in GTV definition. Riegal et al106

demonstrated that contouring on registered CT/PET images without windowing

protocols did not reduce differences in contoured GTVs. Berson et al129 performed a

follow-up study which found that viewing and contouring protocols did show

improvement in the consistency of GTVs contoured by multiple radiologists and

radiation oncologists. The ROs were provided with a tutorial prior to their

participation in the GTV contouring trials for this research project. Berson et al’s129

study also provided a tutorial in their trial prior to participants contouring the patient

images, highlighting the importance of training when any protocols are implemented

to further reduce inter-user variation.

199

5 The outcomes of the quality assurance phantom study were used to develop clinical

protocols which minimised potential errors in the CT/PET fusion process to improve

the accuracy of GTV localisation for 3DCRT lung cancer patients. Image

acquisition, PET viewing, image registration and CT-PET fusion protocols were

developed and tested.

Conclusions and recommendations

• The image acquisition protocol successfully identified candidates for CT/PET

fusion in advance of their staging PET scan to facilitate positioning of the

patient in their radiotherapy treatment position.

• The phantom study determined that PET viewing windows which provided

an accurate display of volumes was dependant on tumour-background ratios.

A protocol based on window widths and levels relative to the maximum

image pixel value was more easily applied to different patient specific

conditions.

• The automated registration can provide sub-voxel levels of accuracy when

the CT and PET images are registered. Fiducial markers were not found to be

of benefit in the image registration process. Image registration protocols

which factored in potential software-based errors combined with adequate

user training are recommended to increase the accuracy and reproducibility of


• A semi-automated adaptive threshold contouring technique accurately defines

the geometric edge of a tumour volume using PET image data from a stand

alone PET scanner, including 4D target volumes. The contouring protocols,

incorporating the PET viewing protocols demonstrated that consistent

primary tumour volume results across different oncologists could be

obtained. However it also demonstrated that variations in the contouring of

regional lymph nodes were not eliminated in some of the patient data studied.

It is recommended that this study is adapted to different treatment planning systems

and for PET image data obtained from combined CT-PET scanners.

200

Plots of the maximum and mean pixel values obtained from the TPS contouring tools for each PET AC image: Series 1-4

Appendix 1: Phantom PET scan pixel and SUV graphs

Plots (a) – (d) are of the maximum pixel value in each of the 3D contours of the different components of the phantom for each AC scan in the indicated PET scan series Plots (e) – (h) are of the mean pixel value in each of the 3D contours of the different components of the phantom on each AC scan in the indicated PET scan series

Phantom PET scan Series 1

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Plots of the maximum and mean pixel values obtained from the TPS contouring tools for each PET AC image: Series 5-8 Plots (a) – (d) are of the maximum pixel value in each of the 3D contours of the different components of the phantom for each AC scan in the indicated PET scan series Plots (e) – (h) are of the mean pixel value in each of the 3D contours of the different components of the phantom on each AC scan in the indicated PET scan series


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Plots of the maximum and mean SUV values obtained from the TPS contouring tools for each PET AC image: Series 1-4 Plots (a) – (d) are of the maximum SUV value in each of the 3D contours of the different components of the phantom for each AC scan in the indicated PET scan series Plots (e) – (h) are of the mean SUV value in each of the 3D contours of the different components of the phantom on each AC scan in the indicated PET scan series


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Plots of the maximum and mean SUV values obtained from the TPS contouring tools for each PET AC image: Series 5-8 Plots (a) – (d) are of the maximum SUV value in each of the 3D contours of the different components of the phantom for each AC scan in the indicated PET scan series Plots (e) – (h) are of the mean SUV value in each of the 3D contours of the different components of the phantom on each AC scan in the indicated PET scan series


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Post registration results of registering the same PET image (series 1 scan (a)) of the phantom with itself using the CC algorithm with varying pre-registration translation and rotation offsets

Appendix 2: Graphs of the registration algorithm test results

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Box plots of post-registration results (difference from baseline registrations) for each registration translation and rotation parameter. Registration was performed using either the PET AC scan or the PET transmission scan.

Appendix 3: Graphs of the phantom registration results

Registrations using PET scan series 1-3 and 5 images (n=6 registrations)

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Registrations using PET scan series 4 images (n=6 registrations)

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RT instructions Appendix 4: RT instructions for the patient image registration trials

General instructions

1. You will be given an identifying name by which all your patient data sets will be labelled

2. There are 9 patient CT and PET data sets. 3. For each patient you are required to register the images

• Manually using the CT and PET emission scan • Automatically using the:

− CT and PET emission scans or − CT, PET transmission and PET emission scans

4. Two plans have been created for each patient. The image data has already been imported for each plan. The two plans have the following names:

• Manual – for the manual registrations • Auto – for the registrations performed using the Syntegra auto-fusion

algorithms 5. You have been provided with some reference information on PET image

reconstruction and interpretation as well as some of the instructions from the Pinnacle user manual for the Syntegra program.

6. You are asked not to compare your outcomes with another participant in the trial.

Viewing and windowing the data sets

1. For the CT data: • Select “Raw” for the Units option. This will result in all CT data

being given as CT numbers • Use the window width and level presets that you would normally use

(Abdomen or breast work well for all of the CT data sets in the trial). 2. For the PET transmission scans:

• Use either the “greyscale” or “thermal” for the 2D colour option • Select “% Max” for the Units option. This will result in all PET data

being represented as a percentage of the value of the scan • Set the window to 0.4 and the level to 0.2 (NB This is a suggested

starting point, you can adjust these settings) 3. For the PET emission scans:

• Use either the “greyscale” or “thermal” for the 2D colour option • Select “% Max” for the Units option. This will result in all PET data

being represented as a percentage of the max value of the scan • Suggested starting window widths and levels for the PET data is as

follows (you can adjust these settings if you wish): − The window to 0.2 and the level to 0 as a starting point when

matching air/tissue surfaces between the CT and PET data − The window to 0.4 and the level to 0 as a starting point when

matching internal features between the CT and PET data

215

Manual registration

1. All of the 9 patients need to be manually registered. You are required to manually translate and rotate the PET emission scan to register it with the planning CT scan.

2. Select the plan named Manual that has been created for each patient and enter the Syntegra platform.

3. In the Set-up window choose the appropriate 2D colour and window width and level as outlined previously. You can adjust the window width and levels suggested to achieve better visualisation of the data if required.

4. You will need to change your window widths and levels of the PET emission scan depending on whether you are trying to match air/tissues surfaces or internal features.

5. Initially you may need to zoom the coronal image down to locate the position of the PET emission scan.

6. Use only the manual 2D and rotation and translation tools. 7. Once you have registered the CT and PET data exit and save the data.

Automatic registration

There are two ways that you will be required to register the data using the Syntegra

auto-fusion algorithms. The method you use to automatically register the patient CT

and PET data depends on whether the patient data includes only a PET emission

scan or includes both a PET transmission and emission scan. The patient plan

information will be labelled accordingly to identify the available data so that you

know which method to use to register the patient.

1. Auto-registering patient data with a CT and PET emission scan • There are 4 patients with only the CT and PET emission scans.

1. Select the plan named “Auto” for the relevant patient. 2. In the Set-up window choose the appropriate 2D colour and window width

and level as outlined previously. You can adjust the suggested window width and levels to achieve better visualisation of the data if required.

3. Click the “Move secondary centre to primary centre” button. This will move the absolute middle of the secondary scan to the absolute middle of the CT data.

4. If the lungs surfaces on the PET data is sitting above the CT data then manually move the PET data so that the lungs on the PET data are roughly aligned with those on the CT data.

5. In the Fusion window select the appropriate registration parameters • Select CT-PET emission • then Normalised Mutual Information • then Proceed with fusion

6. Do not use the Limit image sets functions. 7. Save and exit the plan.

216

2. Auto-registering patient data with a CT and PET transmission and emission scans

• There are 5 patients with CT and PET transmission and emission scans.

1. Select the plan named “Auto” for the relevant patient. 2. You have two secondary images – one is the transmission scan the other is

the emission scan. The transmission scan should be the one with the (2) in front of the scan name (eg. (2)XXXXX, XXXXX). You can select the image you want to work with by clicking on the button next to secondary image in the set-up window and selecting appropriate image set.

3. In the Set-up window choose the appropriate 2D colour and window width and level as outlined previously for all the data sets (CT, PET transmission and emission scans). You can adjust the suggested window width and levels to achieve better visualisation of the data if required.

4. Select the transmission scan ((2)XXXXX, XXXXX) as the secondary data set.

5. Make sure that both data sets available for registration that are shown in the Available fusion image sets are set to Moveable.

6. Click the Move secondary centre to primary centre button. This will move the absolute middle of the secondary scan to the absolute middle of the CT data.

8. If the lungs surfaces on the PET data is sitting above the CT data then manually move the PET data so that the lungs on the PET data are roughly aligned with those on the CT data.

9. In the Fusion window select the appropriate registration parameters • Select CT-PET transmission • then Normalised Mutual Information • then Proceed with fusion

10. In the bottom right hand corner of the Fusion window under Copy this transformation to, select the PET emission scan then press Go.

11. Do not use the Limit image sets functions. 12. Save and exit the plan.

217

Box plots of post-registration results (difference from baseline registrations) for each registration translation and rotation parameter. Registration was performed either manually or automatically using the MI algorithm.

Appendix 5: Graphs of the results for the RT registration trials

Registrations results for patient 1 - 3 (n=12 registrations)

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219


220

Appendix 6: RO instructions for the patient GTV definition trials

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6 1. Maintz JB, Viergever MA. A survey of medical image registration. Med Image

Anal 1998;2(1):1-36.

Bibliography

2. Thornton AF, Jr., Sandler HM, Ten Haken RK, McShan DL, Fraass BA, La Vigne ML, et al. The clinical utility of magnetic resonance imaging in 3-dimensional treatment planning of brain neoplasms. Int J Radiat Oncol Biol Phys 1992;24(4):767-75.

3. Kooy HM, van Herk M, Barnes PD, Alexander E, 3rd, Dunbar SF, Tarbell NJ, et al. Image fusion for stereotactic radiotherapy and radiosurgery treatment planning. Int J Radiat Oncol Biol Phys 1994;28(5):1229-34.

4. Gambhir SS, Czernin J, Schwimmer J, Silverman DHS, Coleman RE, Phelps ME. A tabulated summary of the FDG PET literature. J Nucl Med 2001;42 Suppl(5):1S-93S.

5. Paulino AC, Johnstone PAS. FDG-PET in radiotherapy treatment planning: Pandora's box? Int J Radiat Oncol Biol Phys 2004;59(1):4-5.

6. Keyes JW, Jr. SUV: standard uptake or silly useless value? J Nucl Med 1995;36(10):1836-9.

7. Chen GT, Kung JH, Beaudette KP. Artifacts in computed tomography scanning of moving objects. Semin Radiat Oncol 2004;14(1):19-26.

8. Bakheet SM, Saleem M, Powe J, Al Amro A, Larsson SG, Mahassin Z. F-18 fluorodeoxyglucose chest uptake in lung inflammation and infection. Clin Nucl Med 2000;25(4):273-278.

9. Withers HR. Biological basis of radiation therapy for cancer. Lancet 1992;339(8786):156-9.

10. International Commission on Radiation Units and Measurements (ICRU) Report 50. Prescribing, recording, and reporting photon beam therapy. Bethesda, MD, USA; 1993.

11. International Commission on Radiation Units and Measurements (ICRU) Report 62. Prescribing, recording, and reporting photon beam therapy (supplement to ICRU Report 50). Bethesda, MD, USA; 1999.

12. Purdy JA. Current ICRU definitions of volumes: limitations and future directions. Semin Radiat Oncol 2004;14(1):27-40.

13. Purdy JA. Dose to normal tissues outside the radiation therapy patient's treated volume: a review of different radiation therapy techniques. Health Phys 2008;95(5):666-76.

14. Emami B, Lyman J, Brown A, Coia L, Goitein M, Munzenrider JE, et al. Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol Biol Phys 1991;21(1):109-22.

231

15. Tsujino K, Hirota S, Endo M, Obayashi K, Kotani Y, Satouchi M, et al. Predictive value of dose-volume histogram parameters for predicting radiation pneumonitis after concurrent chemoradiation for lung cancer. Int J Radiat Oncol Biol Phys 2003;55(1):110-5.

16. Fraass BA. The development of conformal radiation therapy. Med Phys 1995;22(11 Pt 2):1911-21.

17. Aird EG, Conway J. CT simulation for radiotherapy treatment planning. Br J Radiol 2002;75(900):937-49.

18. Tyldesley S, Boyd C, Schulze K, Walker H, Mackillop WJ. Estimating the need for radiotherapy for lung cancer: an evidence-based, epidemiologic approach. Int J Radiat Oncol Biol Phys 2001;49(4):973-85.

19. The Royal College of Radiologists Clinical Oncology Information Network. Guidelines on the non-surgical management of lung cancer. Clin Oncol (R Coll Radiol) 1999;11(1):S1-S53.

20. Sause WT. The role of radiotherapy in non-small cell lung cancer. Chest 1999;116(6 Suppl):504S-508S.

21. Marks LB, Sibley G. The rationale and use of three-dimensional radiation treatment planning for lung cancer. Chest 1999;116(6 Suppl):539S-545S.

22. Coleman RE. PET in lung cancer. J Nucl Med 1999;40(5):814-820.

23. Van de Steene J, Linthout N, de Mey J, Vinh-Hung V, Claassens C, Noppen M, et al. Definition of gross tumor volume in lung cancer: inter-observer variability. Radiother Oncol 2002;62(1):37-49.

24. Giraud P, Elles S, Helfre S, De Rycke Y, Servois V, Carette M-F, et al. Conformal radiotherapy for lung cancer: different delineation of the gross tumor volume (GTV) by radiologists and radiation oncologists. Radiother Oncol 2002;62(1):27-36.

25. Senan S, van Sornsen de Koste J, Samson M, Tankink H, Jansen P, Nowak PJ, et al. Evaluation of a target contouring protocol for 3D conformal radiotherapy in non-small cell lung cancer. Radiother Oncol 1999;53(3):247-55.

26. Fritscher-Ravens A, Bohuslavizki KH, Brandt L, Bobrowski C, Lund C, Knofel T, et al. Mediastinal lymph node involvement in potentially resectable lung cancer - Comparison of CT, positron emission tomography, and endoscopic ultrasonography with and without fine-needle aspiration. Chest 2003;123(2):442-451.

27. MacManus MP, Hicks RJ, Ball DL, Kalff V, Matthews JP, Salminen E, et al. F-18 fluorodeoxyglucose positron emission tomography staging in radical radiotherapy candidates with nonsmall cell lung carcinoma. Cancer 2001;92(4):886-895.

232

28. Photon Treatment Planning Collaborative Working Group. Three-dimensional display in planning radiation therapy: a clinical perspective. Int J Radiat Oncol Biol Phys 1991;21(1):79-89.

29. Sherouse GW, Novins K, Chaney EL. Computation of digitally reconstructed radiographs for use in radiotherapy treatment design. Int J Radiat Oncol Biol Phys 1990;18(3):651-8.

30. Galvin JM, Sims C, Dominiak G, Cooper JS. The use of digitally reconstructed radiographs for three-dimensional treatment planning and CT-simulation. Int J Radiat Oncol Biol Phys 1995;31(4):935-42.

31. Thomas SJ. Relative electron density calibration of CT scanners for radiotherapy treatment planning. Br J Radiol 1999;72(860):781-6.

32. Ramm U, Damrau M, Mose S, Manegold KH, Rahl CG, Bottcher HD. Influence of CT contrast agents on dose calculations in a 3D treatment planning system. Phys Med Biol 2001;46(10):2631-5.

33. Rosenman JG, Miller EP, Tracton G, Cullip TJ. Image registration: an essential part of radiation therapy treatment planning. Int J Radiat Oncol Biol Phys 1998;40(1):197-205.

34. Bradley JD, Perez CA, Dehdashti F, Siegel BA. Implementing biologic target volumes in radiation treatment planning for non-small cell lung cancer. J Nucl Med 2004;45 Suppl 1:96S-101S.

35. Baker GR. Localization: conventional and CT simulation. Br J Radiol 2006;79(Special Issue 1):S36-49.

36. Erdi YE, Rosenzweig K, Erdi AK, Macapinlac HA, Hu YC, Braban LE, et al. Radiotherapy treatment planning for patients with non-small cell lung cancer using positron emission tomography (PET). Radiother Oncol 2002;62(1):51-60.

37. Mah K, Caldwell CB, Ung YC, Danjoux CE, Balogh JM, Ganguli SN, et al. The impact of (18)FDG-PET on target and critical organs in CT-based treatment planning of patients with poorly defined non-small-cell lung carcinoma: a prospective study. Int J Radiat Oncol Biol Phys 2002;52(2):339-50.

38. van Herk M. Errors and margins in radiotherapy. Semin Radiat Oncol 2004;14(1):52-64.

39. van Herk M, Remeijer P, Rasch C, Lebesque JV. The probability of correct target dosage: dose-population histograms for deriving treatment margins in radiotherapy. Int J Radiat Oncol Biol Phys 2000;47(4):1121-35.

40. McKenzie A CM, Greemer T, Hall C, Van Herk M, Mijnheer B, Harrison A. Chapter2: Technical overview of geometrical uncertainties in radiotherapy. In: Geometric uncertainties in radiotherapy: The British Institute of Radiology; 2003. p. 11-27.

233

41. Armstrong JG. Target volume definition for three-dimensional conformal radiation therapy of lung cancer. Br J Radiol 1998;71(846):587-94.

42. Fraass B, Doppke K, Hunt M, Kutcher G, Starkschall G, Stern R, et al. American Association of Physicists in Medicine Radiation Therapy Committee Task Group 53: quality assurance for clinical radiotherapy treatment planning. Med Phys 1998;25(10):1773-829.

43. Hutton BF, Braun M, Thurfjell L, Lau DY. Image registration: an essential tool for nuclear medicine. Eur J Nucl Med Mol Imaging 2002;29(4):559-77.

44. Garcia-Ramirez JL, Mutic S, Dempsey JF, Low DA, Purdy JA. Performance evaluation of an 85-cm-bore X-ray computed tomography scanner designed for radiation oncology and comparison with current diagnostic CT scanners. Int J Radiat Oncol Biol Phys 2002;52(4):1123-31.

45. Mutic S, Palta JR, Butker EK, Das IJ, Huq MS, Loo LN, et al. Quality assurance for computed-tomography simulators and the computed-tomography-simulation process: report of the AAPM Radiation Therapy Committee Task Group No. 66. Med Phys 2003;30(10):2762-92.

46. Mulkens TH, Bellinck P, Baeyaert M, Ghysen D, Van Dijck X, Mussen E, et al. Use of an automatic exposure control mechanism for dose optimization in multi-detector row CT examinations: clinical evaluation. Radiology 2005;237(1):213-23.

47. Greess H, Nomayr A, Wolf H, Baum U, Lell M, Bowing B, et al. Dose reduction in CT examination of children by an attenuation-based on-line modulation of tube current (CARE Dose). Eur Radiol 2002;12(6):1571-6.

48. Flohr TG, Schaller S, Stierstorfer K, Bruder H, Ohnesorge BM, Schoepf UJ. Multi-detector row CT systems and image-reconstruction techniques. Radiology 2005;235(3):756-73.

49. Wilting JE, Timmer J. Artefacts in spiral-CT images and their relation to pitch and subject morphology. Eur Radiol 1999;9(2):316-22.

50. Centre for Evidence Based Purchasing. Report 05071 Siemens Somatom Sensation Open CT scanner technical evaluation: NHS Purshasing and Supply Agency; 2005.

51. Heiken JP, Brink JA, Vannier MW. Spiral (helical) CT. Radiology 1993;189(3):647-56.

52. Balter JM, Lam KL. Technical note: acquisition of CT models for radiotherapy applications with reduced tube heating. Med Phys 2001;28(4):590-2.

53. Fahey FH. Data Acquisition in PET Imaging. J Nucl Med Technol 2002;30(2):39-49.

234

54. Huang SC, Hoffman EJ, Phelps ME, Kuhl DE. Quantitation in positron emission computed tomography: 3 Effect of sampling. J Comput Assist Tomogr 1980;4(6):819-26.

55. Meikle S, Dahlbon M. Positron Emission Tomography. In: Murray IPC, Ell PJ, Van der Wall H, editors. Nuclear medicine in clinical diagnosis and treatment. 2nd ed. Edinburgh ; London: Churchill Livingstone; 1998. p. 1603-1638.

56. Hoffman EJ, Huang SC, Phelps ME. Quantitation in positron emission computed tomography: 1. Effect of object size. J Comput Assist Tomogr 1979;3(3):299-308.

57. Kessler RM, Ellis JR, Jr., Eden M. Analysis of emission tomographic scan data: limitations imposed by resolution and background. J Comput Assist Tomogr 1984;8(3):514-22.

58. Mazziotta JC, Phelps ME, Plummer D, Kuhl DE. Quantitation in positron emission computed tomography: 5. Physical--anatomical effects. J Comput Assist Tomogr 1981;5(5):734-43.

59. Brambilla M, Matheoud R, Secco C, Sacchetti G, Comi S, Rudoni M, et al. Impact of target-to-background ratio, target size, emission scan duration, and activity on physical figures of merit for a 3D LSO-based whole body PET/CT scanner. Med Phys 2007;34(10):3854-65.

60. Surti S, Karp JS. Imaging characteristics of a 3-dimensional GSO whole-body PET camera. J Nucl Med 2004;45(6):1040-9.

61. Lonneux M, Borbath I, Bol A, Coppens A, Sibomana M, Bausart R, et al. Attenuation correction in whole-body FDG oncological studies: the role of statistical reconstruction. Eur J Nucl Med 1999;26(6):591-8.

62. Raylman RR, Kison PV, Wahl RL, Kress J, Minohara S, Endo M, et al. Capabilities of two- and three-dimensional FDG-PET for detecting small lesions and lymph nodes in the upper torso: a dynamic phantom study. Patient position verification using CT images. Eur J Nucl Med 1999;26(1):39-45.

63. Strauss LG, Conti PS. The applications of PET in clinical oncology. J Nucl Med 1991;32(4):623-48.

64. Kim CK, Gupta NC, Chandramouli B, Alavi A. Standardized uptake values of FDG: body surface area correction is preferable to body weight correction. J Nucl Med 1994;35(1):164-7.

65. Graham MM, Peterson LM, Hayward RM. Comparison of simplified quantitative analyses of FDG uptake. Nucl Med Biol 2000;27(7):647-55.

66. American Association of Physicists in Medicine Task Group 76: Report 91. The management of respiratory motion in radiation oncology. College Park, MD: American Association of Physicists in Medicine; 2006.

235

67. Seppenwoolde Y, Shirato H, Kitamura K, Shimizu S, van Herk M, Lebesque JV, et al. Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. Int J Radiat Oncol Biol Phys 2002;53(4):822-34.

68. Shirato H, Seppenwoolde Y, Kitamura K, Onimura R, Shimizu S. Intrafractional tumor motion: lung and liver. Semin Radiat Oncol 2004;14(1):10-8.

69. Keall P. 4-dimensional computed tomography imaging and treatment planning. Semin Radiat Oncol 2004;14(1):81-90.

70. Caldwell CB, Mah K, Skinner M, Danjoux CE. Can PET provide the 3D extent of tumor motion for individualized internal target volumes? A phantom study of the limitations of CT and the promise of PET. Int J Radiat Oncol Biol Phys 2003;55(5):1381-93.

71. Bradley J, Thorstad WL, Mutic S, Miller TR, Dehdashti F, Siegel BA, et al. Impact of FDG-PET on radiation therapy volume delineation in non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2004;59(1):78-86.

72. Australian Government Department of Health and Ageing. Medicare Benefits Schedule Book 1 November. 2004; http://www.health.gov.au/internet/mbsonline/publishing.nsf/Content/MBSOnline-2004.

73. Michael G. X-ray computed tomography. Phys Educ 2001(6):442.

74. Von Schulthess GK Kacl G Stumpe DM. The normal PET scan. In: Von Schulthess GK, editor. Clinical positron emission tomography (PET): correlation with morphological cross-sectional imaging. Philadelphia Pa; London: Lippincott Williams & Wilkins; 1999. p. 49-69.

75. Ak I, Stokkel MP, Pauwels EK. Positron emission tomography with 2-[18F]fluoro-2-deoxy-D-glucose in oncology. Part II. The clinical value in detecting and staging primary tumours. J Cancer Res Clin Oncol 2000;126(10):560-74.

76. Delbeke D. Oncological applications of FDG PET imaging: brain tumors, colorectal cancer, lymphoma and melanoma. J Nucl Med 1999;40(4):591-603.

77. Hawkes DJ. Registration methodology: Introduction. In: Hajnal JV, Hawkes DJ, Hill D, editors. Medical image registration. Boca Raton: CRC Press; 2001. p. 11-38.

78. Slomka P. Software approach to merging molecular with anatomic information. Journal of Nuclear Medicine 2004;45(1):36S.

79. Brown LG. A Survey of Image Registration Techniques. ACM Computing Surveys 1992;24(4):325-376.

http://www.health.gov.au/internet/mbsonline/publishing.nsf/Content/MBSOnline-2004

http://www.health.gov.au/internet/mbsonline/publishing.nsf/Content/MBSOnline-2004

236

80. Cai J, Chu JC, Recine D, Sharma M, Nguyen C, Rodebaugh R, et al. CT and PET lung image registration and fusion in radiotherapy treatment planning using the chamfer-matching method. Int J Radiat Oncol Biol Phys 1999;43(4):883-91.

81. Hill D, Batchelor P. Chapter 3: Registration methodology: Concepts and Algorithms. In: Hajnal JV, Hawkes DJ, Hill D, editors. Medical image registration. Boca Raton: CRC Press; 2001. p. 11-38.

82. Mutic S, Dempsey JF, Bosch WR, Low DA, Drzymala RE, Chao KS, et al. Multimodality image registration quality assurance for conformal three-dimensional treatment planning. Int J Radiat Oncol Biol Phys 2001;51(1):255-60.

83. Owen R, Kron T, Foroudi F, Cox J, Zhu L, Cramb J, et al. The detectability and localization accuracy of implanted fiducial markers determined on in-room computerized tomography (CT) and electronic portal images (EPI). Med Dosim 2008;33(3):226-33.

84. van Herk M, Kooy HM. Automatic three-dimensional correlation of CT-CT, CT-MRI, and CT-SPECT using chamfer matching. Med Phys 1994;21(7):1163-78.

85. Studholme C, Hill DL, Hawkes DJ. Automated 3-D registration of MR and CT images of the head. Med Image Anal 1996;1(2):163-75.

86. Studholme C, Hill DLG, Hawkes DJ. Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. Med Phys 1997;24(1):25-35.

87. Rösch P, Netsch T, McNutt T, Shoenbill J, Root P. Syntegra: Automated image registration algorithms. Syntegra™ White Paper: Philips Medical Systems; 2003.

88. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P. Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 1997;16(2):187-98.

89. Maes F, Vandermeulen D, Suetens P. Medical image registration using mutual information. Proceedings of the IEEE 2003;91(10):1699-1722.

90. Slomka PJ, Dey D, Przetak C, Aladl UE, Baum RP. Automated 3-dimensional registration of stand-alone (18)F-FDG whole-body PET with CT. J Nucl Med 2003;44(7):1156-67.

91. Walimbe V, Shekhar R. Automatic elastic image registration by interpolation of 3D rotations and translations from discrete rigid-body transformations. Med Image Anal 2006;10(6):899-914.

237

92. Goerres GW, Kamel E, Heidelberg TN, Schwitter MR, Burger C, von Schulthess GK. PET-CT image co-registration in the thorax: influence of respiration. Eur J Nucl Med Mol Imaging 2002;29(3):351-60.

93. Slomka P. Software approach to merging molecular with anatomic information. J Nucl Med 2004;45(1):36S.

94. Zhang Y, Chu JC, Hsi W, Khan AJ, Mehta PS, Bernard DB, et al. Evaluation of four volume-based image registration algorithms. Med Dosim 2009;34(4):317-22.

95. Daisne J-F, Sibomana M, Bol A, Cosnard G, Lonneux M, Grégoire V. Evaluation of a multimodality image (CT, MRI and PET) coregistration procedure on phantom and head and neck cancer patients: accuracy, reproducibility and consistency. Radiother Oncol 2003;69(3):237-245.

96. Dey D, Slomka PJ, Hahn LJ, Kloiber R. Automatic three-dimensional multimodality registration using radionuclide transmission CT attenuation maps: A phantom study. J Nucl Med 1999;40(3):448.

97. Skalski J, Wahl RL, Meyer CR. Comparison of mutual information-based warping accuracy for fusing body CT and PET by 2 methods: CT mapped onto PET emission scan versus CT mapped onto PET transmission scan. J Nucl Med 2002;43(9):1184.

98. Lavely WC, Scarfone C, Cevikalp H, Li R, Byrne DW, Cmelak AJ, et al. Phantom validation of coregistration of PET and CT for image-guided radiotherapy. Med Phys 2004;31(5):1083-92.

99. Wong JC, Studholme C, Hawkes DJ, Maisey MN. Evaluation of the limits of visual detection of image misregistration in a brain fluorine-18 fluorodeoxyglucose PET-MRI study. Eur J Nucl Med 1997;24(6):642-50.

100. Pekar V, McNutt TR, Kaus MR. Automated model-based organ delineation for radiotherapy planning in prostatic region. Int J Radiat Oncol Biol Phys 2004;60(3):973-80.

101. Munley MT, Marks LB, Scarfone C, Sibley GS, Patz EF, Jr., Turkington TG, et al. Multimodality nuclear medicine imaging in three-dimensional radiation treatment planning for lung cancer: challenges and prospects. Lung Cancer 1999;23(2):105-14.

102. Black QC, Grills IS, Kestin LL, Wong CY, Wong JW, Martinez AA, et al. Defining a radiotherapy target with positron emission tomography. Int J Radiat Oncol Biol Phys 2004;60(4):1272-82.

103. Grills IS, Yan D, Black QC, Wong CY, Martinez AA, Kestin LL. Clinical implications of defining the gross tumor volume with combination of CT and 18FDG-positron emission tomography in non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2007;67(3):709-19.

238

104. Patz EF, Lowe VJ, Hoffman JM, Paine SS, Burrowes P, Coleman RE, et al. Focal pulmonary abnormalities: evaluation with F-18 fluorodeoxyglucose PET scanning. Radiology 1993;188(2):487-490.

105. Erdi YE, Mawlawi O, Larson SM, Imbriaco M, Yeung H, Finn R, et al. Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer 1997;80(12 Suppl):2505-9.

106. Riegel AC, Berson AM, Destian S, Ng T, Tena LB, Mitnick RJ, et al. Variability of gross tumor volume delineation in head-and-neck cancer using CT and PET/CT fusion. Int J Radiat Oncol Biol Phys 2006;65(3):726-32.

107. Steenbakkers RJ, Duppen JC, Fitton I, Deurloo KE, Zijp LJ, Comans EF, et al. Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis. Int J Radiat Oncol Biol Phys 2006;64(2):435-48.

108. National Health and Medical Research Council Australian Research Council Australian Vice-Chancellors’ Committee. National Statement on Ethical Conduct in Human Research. 2007; http://www.nhmrc.gov.au/publications/synopses/_files/e72-jul09.pdf.

109. Boellaard R, Krak NC, Hoekstra OS, Lammertsma AA. Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: a simulation study. J Nucl Med 2004;45(9):1519-27.

110. Brown TF, Yasillo NJ. Radiation safety considerations for PET centers. J Nucl Med Technol 1997;25(2):98-102.

111. Soret M, Riddell C, Hapdey S, Buvat I. Biases affecting the measurements of tumor-to-background activity ratio in PET. IEEE Trans Nucl Sci 2002;49(5):2112-2118.

112. Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rube C, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer. J Nucl Med 2005;46(8):1342-8.

113. Schinagl DA, Vogel WV, Hoffmann AL, van Dalen JA, Oyen WJ, Kaanders JH. Comparison of five segmentation tools for 18F-fluoro-deoxy-glucose-positron emission tomography-based target volume definition in head and neck cancer. Int J Radiat Oncol Biol Phys 2007;69(4):1282-9.

114. Hong R, Halama J, Bova D, Sethi A, Emami B. Correlation of PET standard uptake value and CT window-level thresholds for target delineation in CT-based radiation treatment planning. Int J Radiat Oncol Biol Phys 2007;67(3):720-726.

115. Flohr TG, Schaller S, Stierstorfer K, Bruder H, Ohnesorge BM, Schoepf UJ. Multi-Detector Row CT Systems and Image-Reconstruction Techniques. Radiology 2005;235(3):756-773.

http://www.nhmrc.gov.au/publications/synopses/_files/e72-jul09.pdf

239

116. Pan T, Mawlawi O. PET/CT in radiation oncology. Med Phys 2008;35(11):4955-66.

117. Paquet N, Albert A, Foidart J, Hustinx R. Within-patient variability of (18)F-FDG: standardized uptake values in normal tissues. J Nucl Med 2004;45(5):784-8.

118. Wang Y, Chiu E, Rosenberg J, Gambhir SS. Standardized uptake value atlas: characterization of physiological 2-deoxy-2-[18F]fluoro-D-glucose uptake in normal tissues. Mol Imaging Biol 2007;9(2):83-90.

119. Bland JM, Altman DG. Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat 2007;17(4):571-82.

120. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8(2):135-60.

121. Rousson V, Gasser T, Seifert B. Assessing intrarater, interrater and test-retest reliability of continuous measurements. Stat Med 2002;21(22):3431-46.

122. Yaremko B, Riauka T, Robinson D, Murray B, Alexander A, McEwan A, et al. Thresholding in PET images of static and moving targets. Phys Med Biol 2005;50(24):5969-82.

123. Okubo M, Nishimura Y, Nakamatsu K, Okumura M, Shibata T, Kanamori S, et al. Static and moving phantom studies for radiation treatment planning in a positron emission tomography and computed tomography (PET/CT) system. Ann Nucl Med 2008;22(7):579-86.

124. Erdi YE, Nehmeh SA, Pan T, Pevsner A, Rosenzweig KE, Mageras G, et al. The CT motion quantitation of lung lesions and its impact on PET-measured SUVs. J Nucl Med 2004;45(8):1287-92.

125. Pan T, Mawlawi O, Nehmeh SA, Erdi YE, al e. Attenuation Correction of PET Images with Respiration-Averaged CT Images in PET/CT. J Nucl Med 2005;46(9):1481.

126. Nehmeh SA, Erdi YE, Pan T, Pevsner A, Rosenzweig KE, Yorke E, et al. Four-dimensional (4D) PET/CT imaging of the thorax. Med Phys 2004;31(12):3179-86.

127. MacManus M, Nestle U, Rosenzweig KE, Carrio I, Messa C, Belohlavek O, et al. Use of PET and PET/CT for radiation therapy planning: IAEA expert report 2006-2007. Radiother Oncol 2009;91(1):85-94.

128. Nestle U, Kremp S, Grosu AL. Practical integration of [18F]-FDG-PET and PET-CT in the planning of radiotherapy for non-small cell lung cancer (NSCLC): the technical basis, ICRU-target volumes, problems, perspectives. Radiother Oncol 2006;81(2):209-25.

129. Berson AM, Stein NF, Riegel AC, Destian S, Ng T, Tena LB, et al. Variability of gross tumor volume delineation in head-and-neck cancer using PET/CT

240

fusion, Part II: the impact of a contouring protocol. Med Dosim 2009;34(1):30-5.

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