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Quantitative CT Imaging of the Lung Grand Hyatt San Antonio
San Antonio, Texas March 15, 2014
Program Description This one-day course, co-sponsored by the Society of Thoracic Radiology and the Quantitative Imaging Biomarkers Alliance, will summarize the current state of knowledge regarding the role of quantitative CT of the lungs in diffuse lung disease.
Learning Objectives
At the completion of the course, the attendee will be able to: • Describe the technical parameters recommended for performing quantitative CT of the lung parenchyma in diffuse lung disease • Understand the role of CT as a biomarker of diffuse diseases, including COPD and lung fibrosis • Comprehend newer methods for quantification pulmonary vasculature and lung texture • Understand the emerging role of MRI in the lung Parenchyma
Target Audience
Thoracic radiologists, pulmonologists, imaging scientists.
Credit Information The Society of Thoracic Radiology is accredited by the Accreditation Council for Continuing Medical Education to sponsor continuing medical education for physicians. The Society of Thoracic Radiology (STR) designates this live activity for a maximum of 6.0 AMA PRA Category 1 CreditsTM. Physicians should claim only credit commensurate with the extent of their participation in the activity.
Course Directors
David A. Lynch, MB and Jonathan Goldin, MBChB, PhD
Program 7:30 AM - 8:00 AM Continental Breakfast Moderator: David A. Lynch, MB 8:00 AM - 8:10 AM Quantitative CT: Role of the Quantitative Imaging Biomarkers Alliance Daniel C. Sullivan, MD 8:10 AM - 8:30 AM What Do Clinicans Want from Imagers? George R. Washko, MD 8:30 AM - 8:50 AM QCT of the Airways Sean B. Fain, PhD 8:50 AM - 9:10 AM Clinical Evidence for CT as a Biomarker in COPD Jonathan Goldin, MBChB, PhD 9:10 AM - 9:30 AM QIBA Profile: Computed Tomography: Lung Densitometry Philip F. Judy, PhD 9:30 AM - 10:00 AM Discussion 10:00 AM - 10:20 AM Break Moderator: John D. Newell, Jr., MD 10:20 AM - 10:40 AM QCT in Lung Cancer Screening Pim de Jong, MD, PhD 10:40 AM - 11:00 AM Quantification of Pulmonary Vasculature on CT Raul San Jose Estepar, PhD 11:00 AM - 11:20 AM QCT in Lung Fibrosis David A. Lynch, MB 11:20 AM - 11:40 AM Texture Based Approaches to Image Analysis Joyce D. Schroeder, MD 11:40 AM - 11:50 AM Discussion 12:00 PM - 1:30 PM Sponsored Lunch: Workstation Demonstrations Moderator: Geoffrey D. Rubin, MD 1:30 PM - 2:30 PM Scientific Presentations Moderators: Jonathan Goldin, MBChB, PhD and Raul San Jose Estepar, PhD 2:30 PM - 2:50 PM MRI of the Lung Yoshiharu Ohno, MD, PhD 2:50 PM - 3:10 PM Break 3:10 PM - 3:30 PM Quantitative CT of the Lung in Normal Subjects Eric A. Hoffman, PhD 3:30 PM - 4:15 PM Panel Discussion: What is Necessary to Bring QCT into your Reading Room All Speakers
Scientific Oral Presentations Moderators: Jonathan Goldin, MBChB, PhD and Raul San Jose Estepar, PhD
1 1:30 PM Size and Collapsibility of Emphysema Holes on CT of COPD: Evaluation with a New Method Joon Beom Seo, MD, PhD Seo JB, Oh SY, Lee M, Kim N, Lee SM
2 1:40 PM Automated Nodule Segmentation With Sub-voxel Accuracy Using Mutual Interaction of Pulmonary Segmentation Structures Ipek Oguz, PhD Oguz I, Raffy P, Wood S
3 1:50 PM Computer-aided Classification of Diffuse Lung Disease Opacities by Use of Sparse Representation-based Method on 3D-CT Images Shoji Kido, MD, PhD Kido S, Zhao W, Xu R, Hirano Y
4 2:00 PM Dose Modulation in an Anthropomorphic Chest Phantom and its Relative Effects when using Single and Dual Energy Scan Modes John D Newell Jr, MD Newell JD, Sieren JP, Guo J, Levy J, Hoffman EA
5 2:10 PM Lung Lesion Volume Measurements in Simulated Reduced-dose CT Scans Stefano Young, PhD Young S, Kim G, McNitt-Gray M
6 2:20 PM Determination of the Optimal Time-Window to Perform Wide Volume Dual Energy CT in a Swine Model of Pulmonary Embolism (PE) Laura Jimenez-Juan, MD Jimenez-Juan L, Dey C, Moghe S, Mehrez H, Homampour S, Paul NS
Scientific Poster Presentations
Bijan Image Quality Comparison of Low KV Imaging in CT Pulmonary Angiography Bijan B, Belashabadi S, Davoodi M, Kabir A
Jimenez-Juan
Dynamic Lung Perfusion with Wide Volume CT: Effect of Arterial Input on Arterial Flow Maps Jimenez-Juan L, Dey C, Homampour S, Merhez H, Paul NS
Jimenez-Juan
Dynamic Lung Perfusion with Wide Volume CT: Impact of Bolus Length on Arterial Flow Values Jimenez-Juan L, Dey C, Homampour S, Merhez H, Paul NS
Kicska Validation of Simulated Tomosynthesis: Can it Replace Real Imaging for First Line Clinical Trials? Kicska G
Scalzetti Feasibility of Cardiac Output Measurements Based on CT Timing Bolus Imaging Using a Modified Timing Bolus Scalzetti EM, Rajebi H, Ogden KM
Scalzetti Reproducibility of Cardiac Output Measurements Based on CT Timing Bolus Imaging Using a Modified Timing Bolus Scalzetti EM, Rajebi H, Ogden KM
Shim Distensibility Differs Regionally in Asthma vs. Normal Controls: A CT Investigation in SARP subjects Shim SS, Schiebler ML, Sorkness RL, Jarjour NN, Kanne J, Fain SB
Shim Airway Distensibility in Asthma Patients by Computed Tomography: Correlation with Clinical Indices and Reversibility after Bronchodilator Therapy Shim SS, Schiebler ML, Sorkness RL, Jarjour NN, Denlinger LC, Fain SB
Silva Lobar Contribution to Lung Volume and Heterogeneity in Healthy Subjects: Quantitative Assessment by CT Silva M, Nemec S, Dufresne V, Bankier AA
Quantitative CT:Role of the Quantitative Imaging
Biomarkers Alliance
D. Sullivan, MDDuke University;
RSNA
Why Must Imaging Become More Quantitative?
• Molecular medicine (personalized medicine)requires quantitative test results.
E id b d di i & QA P• Evidence-based medicine & QA Programsdepend on objective data.
• Decision-support tools (CADx, CDSS) need quantitative input.
Premise
• Variation in clinical practice results in poorer outcomes and higher costs.
RSNA’s Perspective:
• Extracting objective, quantitative results from imaging studies will g gimprove the value of imaging in
clinical practice.
ATS Policy Statement
An Official Research Policy Statement of the American Thoracic Society/European Respiratory Society: Standards for Quantitative Assessment of Lung Structure• Advances in CT technology have reduced the time for whole lung
imaging to 5 to 10 seconds, fueling a growing demand for rigorous validation of CT-derived quantitative measures in application to d /d i di ll f t d t tdrug/device discovery as well as safety and outcomes assessment.
• With the rapid progress in genome-wide searches, there is an additional need to use these quantitative measures along with characteristic pathology to establish disease phenotypes and to identify gene associations.
• 412 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 181 2010
Quantitative Imaging Biomarkers Alliance (QIBA): Background
• Started in 2007• Mission: Improve value and practicality of
quantitative imaging biomarkers by reducing variability across devices, patients, and time.y p
Collaborating Stakeholders
• Pharmaceutical companies• Imaging device companies• Imaging informatics companies• Government agencies• Professional societies• Clinical trialists and clinicians
QIBA Committees
Quantitative Magnetic Resonance Imaging [Q-MR] Perfusion, Diffusion, and Flow-MRI (PDF-MRI) Functional MRI (fMRI)
Quantitative Computed Tomography [Q-CT] CT Volumetry in Solid Tumors and Lung Nodules CT Densitometry in COPD Airway Morphology in Asthma
Quantitative Nuclear Medicine [Q-NM] FDG-PET SUV
Quantitative Ultrasound [Q-US] Shear Wave Speed for liver fibrosis
QIBA Approach
I. Identify Sources of Error and Variation.II. Specify Potential Solutions in the form of
QIBA “Profiles”QIBA Profiles .III. Test those Solutions. IV. Promulgate Solutions (Profiles) to Vendors
and Users.
1. Image acquisition variability2. Radiologist/Reader variability3 Measurement method variability
Variability in imaging measurements is related to:
3. Measurement method variability
10
Assays are characterized by their:• Technical Performance
Imaging Assays
• Clinical Performance Clinical validation Clinical utility
QIBA Profiles
A QIBA Profile describes a specific performance Claim and how it can be achieved.
QIBA Claim Template• List Biomarkers/Measurand(s)• Specify: Cross-sectional vs. Longitudinal
measurement• List Indices:
– Bias Profile (Disaggregate indices) – Precision Profile
• Test-retest Repeatability (Repeatability coefficient)• Reproducibility (Reproducibility coefficient; Intra-class
Correlation Coefficient [ICC]; Concordant Correlation Coefficient [CCC]).– Specify conditions, e.g.,
» Measuring System variability (hardware & software)
» Site variability» Operator variability (Intra- or Inter-reader)
• Clinical Context
True Biologic Change …
• … is approximately twice the test-retest variability
Cli i l Si ifi f th t h• Clinical Significance of that change needs to be determined by clinical studies.
Thank you.
What Do Clinicians Want from Imagers?g
George Washko M.D.
Brigham and Women’s Hospital
2014
Consultancy Agreements
• Merck
• GlaxoSmithKline
• MedImmune
• Spiration
• Intellent Therapeutic Market Reports
A Clinician’s Basic Needs
• What does the patient have?
• How am I going to treat it?
Broad range of disability for any FEV1
Copyright ©2001 BMJ Publishing Group Ltd.
Jones, P W Thorax 2001;56:880-887
FEV1 35% Predicted FEV1 35% PredictedLongitudinal Change in FEV1
N Engl J Med 2011;365:1184-92
lobal Initiative for Chronicbstructivelobal Initiative for Chronicbstructive
GOGO
ungiseaseungisease
LDLD
© 2013 Global Initiative for Chronic Obstructive Lung Disease
Combined Assessment of COPD
k f Air
flow
Lim
itatio
n)
sk on h
isto
ry)
> 2 (C) (D)4
3
Ris
(GO
LD C
lass
ifica
tion
of Ris
(Exa
cerb
atio
1
0
(A) (B)
mMRC 0-1CAT < 10
2
1
mMRC > 2CAT > 10
Symptoms(mMRC or CAT score)
© 2013 Global Initiative for Chronic Obstructive Lung Disease
How can imaging help the clinician?
• Easy to use diagnostic
• Widely available
• Guides selection of therapy
• Immediately responsive to intervention
• Is cheaper and safer than trial/error
• Where to integrate imaging as a solution?
Emphysema and Lung Function
FEV1: 87% predicted
No spirometric obstruction
FEV1: 80% predicted
No spirometric obstruction
Why use imaging?
Do we need imaging?
• Heterogeneous disease process
• Heterogeneous response to treatmentSpirometry– Spirometry
– Symptoms
– AECOPD
• Outcomes require large cohorts followed over long periods of time
Imaging needs to demonstrate utility
• Must overcome nihilistic attitude about cause of COPD
• Provide new insight into disease pathogenesis and progressionp g
• Show clinical value– Does current battery of treatments warrant imaging?
– You will be doing the clinical trials• Engage Clinicians, NIH, industry
Imaging Solutions
• What is the mechanism for susceptibility?
– Innate lung structure
– Inflammation/repair
Wh t i bi k f i t t?• What are unique biomarkers of interest?
– Markers of pathobiology
– Druggable targets
5
6
7
8
9
10
Voxe
ls
Quantification of emphysema on chest CT
0
1
2
3
4
-1000 -900 -800 -700 -600 -500
HU
% V
Hayhurst Lancet 1984, Müller Chest 1988, Gould ARRD 1988, Gevenois AJRCCM 1995, Coxson AJRCCM 1999; Slide from H. Coxson
Disease Progression
• Progression of emphysema
– Accurate to 1 HU annually?
– Variability of inspiratory effort
Volume correction (conservation of lung mass)– Volume correction (conservation of lung mass)
• What about the changes in intraparenchymal blood volume due to respiratory?
Therapeutic Implications: A1AT Deficiency
Comparison AAT-treated versus placebo-treated subjects using combined data from 2 randomized, double-blind, placebo-controlled trials (n=119)
Mean f/u 2.5 years
Annual decline -1.73 g/L/yr vs. -2.74 g/L/yr in AAT-treated vs. placebo-treated subjects
Stockley, et al. Respiratory Research 2010;11:136
Noble Gas MRI
Yablonski et al. Journal of Applied Physiology
Bronchiectasis in COPD – Selection Criteria
• Prevalence of bronchiectasis 57% (cohort n=201) )
• Bronchiectasis associated with increased risk of death HR 2.54
Martinez-Garcia, et al. AJRCCM 2013
Imaging Solutions
• You cannot develop a great tool and expect someone to use it– Standardize the product
– Engage clinicians and find out what they needg g y
– Carry out clinical trials with standard and image based endpoints
• Goal: Prove that it is better than the current standard
5
Why use imaging?
Biologic Therapy A
Biologic Therapy B
Can imaging prompt screening/treatment for another
condition?
Atlas of COPD, Springer Science. 2008
Quantitative CT of the Airways Sean B Fain, PhD University of Wisconsin - Madison
2012/316049. Epub 2012 Jan
Quantitative CT of the Airways
Sean B. Fain, PhD Departments of Radiology and Medical Physics
University of Wisconsin - Madison
Faculty Disclosures STR – 2014 / San Antonio
Sean B. Fain
• Personal financial interests in commercial entities that
are relevant to my presentation(s) or other faculty roles:
GE Healthcare Research Grant (Current)
Xemed, LLC Scientific Advisory Board/Consultant (Current)
Educational Goals
• Describe the technical parameters recommended for performing quantitative CT of the lung parenchyma in diffuse disease
• Understand the role of CT as a biomarker of diffuse diseases, including COPD and lung fibrosis
• Comprehend newer methods for quantification of pulmonary vasculature and lung texture
• Understand the emerging role of MRI in the lung parenchyma
What is the role for imaging? • There is a gap in our knowledge of disease
progression – Regional inflammatory biomarkers – Regional airway remodeling biomarkers
• Goal for non-invasive regional assessment – Phenotyping severity (cross-sectional) – Monitoring progression (longitudinal) – Quantifying spatial heterogeneity
Airways: The Extracellular Matrix (ECM)1
Courtesy Dr. Robert Brown, Johns Hopkins University
High Resolution X-ray CT: Bronchodilation
Normal Abnormal Normal
Mild/ Moderate
Severe
1A. Shifren, et al. J Allergy (Cairo). 2012;2012:316049. doi: 10.1155/ 19. FRC baseline FRC post-albuterol
Quantitative CT of the Airways Sean B Fain, PhD University of Wisconsin - Madison
6
*
Challenges: Sources of Variability Overview of Quantitative CT Task
• Acquisition Protocol
– Parameters and CT scanner type/vendor – Low radiation dose protocol desired
• Spatial resolution limitations
Multi-Detector Scanner
Slip Ring Enables
Fast Gantry Rotation
Multiple Detector Row Acquired Simultaneou
Up to 4cm (64 rows) coverage
X-ray source
– Overestimation of airway wall (esp. smaller airways of ~2 mm diameter)
– Limited representation of distal airway segments
Airway Segmentation and Measurement
Image Data ~0.5 GB 0.5 X 0.5 X 0.6-0.7 mm3
TLC and FRC Lung Volumes
lume Coverage Achieved with a helical
Acquisition
• Short Breath-Hold time (<10 s) • 3D Isotropic Spatial Resolution
Stack of CT-slices of the lungs
acquired in 5 s breath-hold
Typical Acquisition Parameters2
• Breath-hold Coaching – Desire to scan at a known lung volume—for example, TLC, FRC, or RV.
• Desire 3D sub-millimeter near-isotropic resolution in the x, y, and z axes. – Use an optimal reconstruction kernel – Patient positioned at isocenter – Short breath-hold
• 120 kV
• 0.984 pitch; Rotation Speed: 0.5 s
• 40 mm collimation
• Use the lowest possible x-ray dose that meets the needs of a given research study. – 50-200 mAs (~1.5-8.0 mSv)
Airway Measurement
Full Width and Half Maximum (FWHM)3-5 Limitations of FWHM
Tube Current Scaled to BMI
2Development of Quantitative Computed Tomography Lung Protocols. Newell, John; Jr MD, FACR; Sieren, Jered; BS, RTR; MR, CT; Hoffman, Eric. Journal of Thoracic Imaging. 28(5):266‐271, September 2013.
3Israel Amirav, Sandra S. Kramer, Michael M. Grunstein, and Eric A. Hoffman, J. Appl Physiol 1993 Nov;75(5):2239-50. 4Gregory G. King, Nestor L. Müller, Kenneth P. Whittail, Qing-Sang Xiang, and Peter D. Paré. Am J Respir Crit Care Med 161: 574-580 (2000). 5Nakano; Jonathan C. Wong; Pim A. de Jong; Lilliana Buzatu; Taishi Nagao; Harvey O. Coxson; W. Mark Elliott; James C. Hogg; Peter D. Paré; Am J Respir Crit Care Med 2005, 171, 142-146. 6Joseph M. Reinhardt, Neil D. D’Souza, and Eric A. Hoffman. IEEE Trans on Mediical Imagiing 16(6):820-827.
Model-Based Airway Measurement6 Wall Thickness Measures
in Severe Asthma7
Wall Area % Wall Thickness %
60 * 59 58
57
56 55
54
53 52
Normal Mild-Moderate Severe
19
18.5
18
17.5
17
16.5
16
15.5
15
Normal Mild-Moderate Severe
WA % vs. Normal: p = 0.003
vs. MM: p = 0.005
WT % vs. Normal: p = 0.031
vs. MM: p = 0.014
6Joseph M. Reinhardt, Neil D. D’Souza, and Eric A. Hoffman. IEEE Trans on Mediical Imagiing 16(6):820-827. 7Aysola, et al. “Airway remodeling measured by multidetector CT is increased in severe asthma and correlates with pathology.” Chest 134 (6 ) (2008): 1183-91.
Quantitative CT of the Airways Sean B Fain, PhD University of Wisconsin - Madison
WA
%
WA
%
Airway Segmentation8
Regional Airway Wall Morphology
Seeded Region Growing Algorithm
Inspiratory Image Segmentation without Intensity Correction
Segmentation with Intensity Correction
WA% difference coded by airway
Severe > Non-Severe
Segmentation
without Linearity
Constraint
Segmentation with Linearity
Constraint
Airway Skeleton 8Tschirren J, Hoffman EA, McLennan G, Sonka M. IEEE Trans Med Imaging. 2005 Dec; 24(12):1529-39.
9SB Fain et al., Am. J. Respir. Crit. Care Med. , 2009. 179: p. A5575.
Regional Airway Wall Morphology
Non-Severe > Normal
WA% difference coded by airway segment
9SB Fain et al., Am. J. Respir. Crit. Care Med. , 2009. 179: p. A5575.
Regional Airway Wall Morphology
Severe > Normal
WA% difference coded by airway segment
9SB Fain et al., Am. J. Respir. Crit. Care Med. , 2009. 179: p. A5575.
Baseline
Longitudinal Changes in Severe Asthma
Year 2
Ongoing Clinical Research Studies
0.70
0.68
0.66
0.64 *
†
* p = 0.003 Severe vs.. normal
Severe
Nonsevere
Normal
0.70
0.68
0.66
* 0.64
Severe
Nonsevere
Normal
• Quantitative measures of airways have
established application in asthma and COPD phenotyping
– Severe Asthma Research Program (SARP)7 0.62
0.60
0.58
p = 0.022 Severe vs. mild-to-moderate
† p = 0.039 Severe vs. normal p = 0.008 Severe vs. mild-to-moderate
3 4 5 6
Generation
0.62
0.60
0.58
* p = 0.006 Severe asthma vs.. normal
p = 0.003 Severe asthma vs.. mild-to-moderate
3 4 5 6
Generation
– Spiromics11
– COPD Gene12 7Aysola, et al. “Airway remodeling measured by multidetector CT is increased in severe asthma and correlates with pathology.” Chest 134 (6) (2008): 1183-91. 11Kim, Woo Jin, et al. "CT metrics of airway disease and emphysema in severe COPD." Chest 136.2 (2009): 396-404 12Washko, George R., et al. "Airway wall attenuation: a biomarker of airway disease in subjects with COPD." Journal of
10Witt C. et al., Am J Respir Crit Care Med 183;2011:A372. Applied Physiology 107.1 (2009): 185-191.
Quantitative CT of the Airways Sean B Fain, PhD University of Wisconsin - Madison
MTF
(v)
Software Packages - Translation13-15
13VIDA Diagnostics, Coralville, IA (http://www.vidadiagnostics.com/) 14Fraunhofer MeVIS, Bremen, Germany (http://www.mevis.fraunhofer.de) 15Airway Inspector, Brigham and Women's Hospital, Boston, MA (http://www.airwayinspector.org/)
QIBA COPD/Asthma Subcommittee Future Directions
• Improve consistency and accuracy – Protocol standardization – Improve spatial resolution protocols
• Reduce radiation dose – Longitudinal progression of disease with
multiple lung volumes acquired – Explore iterative statistical and model-based
reconstructions
Overestimation of Airway Wall COPDGene 2 Phantom
Histology
CT
2 mm diameter
16Yasutaka Nakano; Jonathan C. Wong; Pim A. de Jong; Lilliana Buzatu; Taishi Nagao; Harvey O. Coxson; W. Mark Elliott; James C. Hogg; Peter D. Paré; Am J Respir Crit Care Med 2005, 171, 142-146.
COPDgene Study Quality Assurance Phantom (CTP698) 1
Airway Diameter Thickness WA%
1 6 1.5 55.56 2 3 0.6 48.98 3 6 0.9 40.83 4 2.5 0.4 42.61 5 6 1.2 48.98 6 2.5 0.4 42.61
1The Phantom Laboratory, Salem, NY
Reconstruction kernel MTF Examples
Airway Measures with ASIR and Edge Kernel
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
Soft
Standard
Detail
Bone
Bone Plus
Edge
Lung
ASIR with Edge Kernel (100 mAs Half DFOV)
4 5
2.5 mm diameter “airway”
4
0.2
0.0 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0
v (cycles/cm)
Quantitative CT of the Airways Sean B Fain, PhD University of Wisconsin - Madison
Airway Profiles Adaptive Statistical Iterative Reconstruction (ASIR) vs. Filtered Back Projection (FBP)
Standard
17 Rodriguez, A. et al; RSNA Nov. 29 2012, Chicago, IL (rsna2012.rsna.org/search/event_display.cfm?em_id=12034515)
Summary Conclusions
• Airway measurement using MDCT has advanced to practice – Active in ongoing several large multi-center clinical research
studies Software for measurement commercial and academic – are available
• Challenges remain – Repeatability – Precision and accuracy of wall measures
• Especially for more distal airways (~2 mm in diameter)
– Improvement in the context of dose reduction • Iterative statistical and model based reconstructions show promise
Thank you.
Questions?
• Funding:
Acknowledgements
• Duke University
References [1] A. Shifren, et al. J Allergy (Cairo). 2012;2012:316049. doi: 10.1155/2012/316049. Epub 2012 Jan 19.
[2] Newell, John; Jr MD, FACR; Sieren, Jered; BS, RTR; MR, CT; Hoffman, Eric. Journal of Thoracic Imaging. 28(5):266‐271, September 2013.
– QIBA Contract: NIBIB-PB-EB-1010- 159-JKS
– The Hartwell Foundation – NIH/NHLBI R01 HL080412 – T32-CA009206 – Vertex Pharmaceuticals
Physics
– Kevin Johnson, PhD – Grzegorz Bauman, PhD – Robert Cadman, PhD – Dr. Jie Tang, PhD for IT and
Reconstruction Support
• Pulmonology – Ronald Sorkness, PhD – Nizar Jarjour, MD – Loren Denlinger, MD – Michael Rock, MD
– Bastiaan Driehuys, PhD – Scott Robertson – Sivaram Kaushik
• Radiology
– Scott Nagle, MD – Brad Maxfield, MD
• Nurse coordinator
– Jan Yakey, RN – Jennifer Swartz, RN
• Research Technologists
– Kelli Hellenbrand, RT – Sara Pladziewicz, RT – Alice Minx, RT
[3] ISRAEL AMIRAV, SANDRA S. KRAMER, MICHAEL M. GRUNSTEIN, AND ERIC A. HOFFMAN, J. Appl Physiol 1993 Nov;75(5):2239-50.
[4] GREGORY G. KING, NESTOR L. MÜLLER, KENNETH P. WHITTALL, QING-SAN XIANG, and PETER D. PARÉ. Am J Respir Crit Care Med 161: 574-580 (2000).
[5] Nakano; Jonathan C. Wong; Pim A. de Jong; Lilliana Buzatu; Taishi Nagao; Harvey O. Coxson; W. Mark Elliott; James C. Hogg; Peter D. Paré; Am J Respir Crit Care Med 2005, 171, 142-146.
[6] Joseph M. Reinhardt,* Member, IEEE, Neil D. D’Souza, and Eric A. Hoffman. IEEE Trans on Mediical Imagiing 16(6):820-827.
[7] Aysola, et al. “Airway remodeling measured by multidetector CT is increased in severe asthma and correlates with pathology.” Chest 134 (6) (2008): 1183-91
[8] Tschirren J, Hoffman EA, McLennan G, Sonka M. IEEE Trans Med Imaging. 2005 Dec;24(12):1529-39. [9] SB Fain et al., Am. J. Respir. Crit. Care Med. , 2009. 179: p. A5575. [10] Witt C. et al., Am J Respir. Crit. Care Med 183;2011:A372. [11] Kim, Woo Jin, et al. "CT metrics of airway disease and emphysema in severe COPD." Chest 136.2 (2009): 396-404 [12] Washko, George R., et al. "Airway wall attenuation: a biomarker of airway disease in subjects with COPD." Journal
of Applied Physiology 107.1 (2009): 185-191. [13] VIDA Diagnostics, Coralville, IA (http://www.vidadiagnostics.com/) [14] Fraunhofer MeVIS, Bremen, Germany (http://www.mevis.fraunhofer.de) [15] Airway Inspector, Brigham and Women's Hospital, Boston, MA (http://www.airwayinspector.org/) [16] Yasutaka Nakano; Jonathan C. Wong; Pim A. de Jong; Lilliana Buzatu; Taishi Nagao; Harvey O. Coxson; W. Mark
Elliott; James C. Hogg; Peter D. Paré; Am J Respir Crit Care Med 2005, 171, 142-146. [17] Rodriguez, A. et al; RSNA Nov. 29 2012, Chicago, IL
(rsna2012.rsna.org/search/event_display.cfm?em_id=12034515)
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QIBA Profile: Computed Tomography: Lung Densitometry
Philip F. Judy, PhD
Brigham and Women’s Hospital
andand
Harvard Medical School
Boston, [email protected]
ChairRSNA QIBA COPD/Asthma Technical
Committee
COPD/Asthma Technical CommitteeLung parenchyma density profile
• A QIBA Profile is a document that describes a specific performance claim and how it can be achieved.Details : http://qibawiki.rsna.org/index.php?title=Main_Page
• Primary claim of the lung parenchyma density profile will be a consistency performance claim (1 HU, which i li 10% i t i “ l” f ti l thimplies 10% consistency in “normal” fraction less than ‐950 HU)
• Investigators have reported inconsistent lung parenchyma density measurements associated with:– scanner model– reconstruction protocol– radiation dose (mAs)– slice thickness.
Selected Inconsistency Reports• Scanner Model: B.C. Stoel, F. Bode, A. Rames, S. Soliman, J.H. Reiber, J. Stolk,
"Quality control in longitudinal studies with computed tomographic densitometry of the lungs," Proc Am Thorac Soc 5, 929‐933 (2008).
• Reconstruction protocol: K.L. Boedeker, M.F. McNitt‐Gray, S.R. Rogers, D.A. Truong, M.S. Brown, D.W. Gjertson, J.G. Goldin, "Emphysema: effect of reconstruction algorithm on CT imaging measures," Radiology 232, 295‐301 (2004).
• Radiation Dose (mAs): R. Yuan, J.R. Mayo, J.C. Hogg, P.D. Pare, A.M. McWilliams, S. Lam, H.O. Coxson, "The effects of radiation dose and CT manufacturer on measurements of lung densitometry," Chest 132, 617‐623 (2007).
• Slice Thickness: D.S. Gierada, A.J. Bierhals, C.K. Choong, S.T. Bartel, J.H. Ritter, N.A. Das, C. Hong, T.K. Pilgram, K.T. Bae, B.R. Whiting, J.C. Woods, J.C. Hogg, B.A. Lutey, R.J. Battafarano, J.D. Cooper, B.F. Meyers, G.A. Patterson, "Effects of CT Section Thickness and Reconstruction Kernel on Emphysema Quantification Relationship to the Magnitude of the CT Emphysema Index," Acad Radiol 17, 146‐156 (2010)
QIBA Profile Elements for Discussion
1. Specialized QC Phantom – Water phantom is not sufficient1. J.P. Sieren, J.D. Newell, P.F. Judy, D.A. Lynch, K.S. Chan, J. Guo, E.A. Hoffman,
"Reference standard and statistical model for inter‐site and temporal comparisons of CT attenuation in a multicenter quantitative lung study," Med Phys 39, 5757‐5767 (2012).
2. Breathing coaching and instructions3 Pixel noise specified3. Pixel noise specified
a. While pixel noise biases measurement ‐ constant tube current is specified (Pixel standard deviation = 20 HU, approximately 50 mAs)
b. No AEC c. No dose reduction reconstructionsd. Rather, pixel noise is precisely specified
HPR01 - LightSpeed 16
-1020-1015-1010-1005-1000-995-990-985-980
3/28/2009 1/22/2010 11/18/2010
Date
CT
Num
ber Inside Air
Outside Air
Lung - HPR01 - LightSpeed 16
-875
-870
-865
-860
-855
-850
-845
-840
-835
3/28/2009 10/14/2009 5/2/2010 11/18/2010
Date
Lung
CT
Num
ber
1. Specialized QC Phantom Water CT number unlikely to identify important changes associated with recalibration
Date
Material ShiftInside Air -19.78Outside Air -19.94Lung -18.05Water -0.96Acrylic -0.30
Inside air after = -1013.4
Note: This recalibration was an “over-correction”.
Air CT Numbers Inside and Outside COPDGene PhantomThree Clusters
GE LightSpeed16, Brilliance 64, Other Scanners
-1000
-995
-990
T N
umbe
r
Brilliance 64Definition
f S
-1015
-1010
-1005
-1010 -1005 -1000 -995 -990 -985 -980
Air Outside CT Number
Air
Insi
de C
T Definition AS+LightSpeed VCTSensation 16Sensation 64GE 16 Slice
-1000
-998
-996
-994
CT
Num
ber
Brilliance 64DefinitionDefinition AS+
Each point is data from a single scanner in the COPDGene StudyConcluded: 1. For these scanner models using outside air CT number will not be useful correction for these CT scanner variations. 2. Intra‐model performance is small.
-1008
-1006
-1004
-1002
-1008 -1006 -1004 -1002 -1000 -998 -996 -994
Air Outside CT Number
Air
Insi
de C
LightSpeed VCTSensation 16Sensation 64
2. Breathing coaching and instructions• For this scan, I am going to ask you to take a couple of
deep breaths in and out before we have you breathe all the way in and hold your breath.
• OK, let’s get started, • Take a deep breath in (watch chest to ensure a deep breath
in) • Let it out (watch chest to ensure air is out) • Take a deep breath in (watch chest to ensure a deep breath
in) • Let it out (watch chest to ensure air is out) • Now breathe all the way IN...IN...IN (watch chest to ensure
a deep breath in as far as possible) • Keep holding your breath – DO NOT BREATHE! • Count 10 seconds (if practice) or scan; • At end of scan or practice: Breathe and relax
Pixel standard deviation of water and acylicregions of the COPDGene Phantom
• Data from Sieren JP, Newell JD, Judy PF, Lynch DA, Chan KS, Guo J, Hoffman EA., Reference standard and statistical model for inter site and temporal
3. Pixel noise specified : Background
statistical model for inter‐site and temporal comparisons of CT attenuation in a multicenter quantitative lung study. Med Phys. 2012 Sep;39(9):5757‐67.
• Expiration protocol – 50 mAs , 120 kVp
• 47 scanner models
• 331 scans
y = ‐9.02x + 26.8R² = 0.27
20
25
30
Deviation [HU]
Water
COPDGene Phantom ‐ 50mAs47 CT Scanners
3. Pixel noise specified : Background
y = ‐11.62x + 28.4R² = 0.39
0
5
10
15
0.4 0.5 0.6 0.7 0.8 0.9 1
Pixel Standard D
Slice Thickness [mm]
Water
Acylic
Linear (Water)
Linear (Acylic)
y = 1.01x ‐ 0.56R² = 0.89
22
24
26
eviation [HU]
COPDGene Phantom ‐ 50mAs47 CT Scanners
3. Pixel noise specified : Background
14
16
18
20
14 16 18 20 22 24 26
Acrylic Pixel Standard De
Water Pixel Standard Deviation [HU]
22
24
26
Deviation [HU]
Brilliance 64
Definition
COPDGene Phantom ‐ 50mAsSelected CT Scanners
At least 3 scanners of same model
3. Pixel noise specified : Background
14
16
18
20
14 16 18 20 22 24Water Pixel Standard
Acrylic Pixel Standard Deviation [HU]
LightSpeed VCT
LightSpeed16
Sensation16
11
12
13
Pixel Standard
n [HU]
Brilliance 64
COPDGene Phantom ‐ AcrylicSelected CT Scanners
At least 3 scanners of same model
3. Pixel noise specified : Background
8
9
10
14 16 18 20 22 24 26
200 m
As Acrylic P
Deviation
50 mAs Acrylic Pixel Standard Deviation [HU]
Definition
LightSpeed VCT
LightSpeed16
Sensation 16
An approach to evaluate the effect of the variation of pixel standard deviation on fraction less than ‐950 HU
• Model of histogram – two components– Lung has two classes of pixels: Parenchyma and NonParenchyma
P h i l h G i hi t– Parenchyma pixels have a Gaussian histogram
– All NonParenchyma pixels are greater than the mode of lung histogram
• Sigma of Gaussian of parenchyma pixel has two components– Biological and measurement noise
– Measurement noise is pixel standard deviation
200000
250000
300000m Value
Case measured with Brilliance 64 – Fraction less than ‐950 = 7.8%Gaussian sigma = 41 HURed curve is lung parenchymaBlue curve is lung non‐parenchyma (partial volume of vessels
0
50000
100000
150000
‐1050 ‐950 ‐850 ‐750 ‐650 ‐550
Histogram
CT Number [HU]
Histogram
Fit Gaussian
Difference Above Mode
60
70
80
90
Fit [HU]
3. Pixel noise specified : Background31 Cases – Same CT scannerIncrease BMI reduces detected x‐rays leading to higher noise
y = 1.3832x + 5.5912R² = 0.3402
0
10
20
30
40
50
0 10 20 30 40 50
Sigm
a of Gassian
F
Body Mass Index
Sigma
Linear (Sigma)
150000
200000
250000
300000
gram
Value
Sigma=41
Sigma=51
Sigma = 41 Fraction less than ‐950 = 7.8%Scanner with a pixel standard deviation 10 HU larger=> Sigma = 51 Fraction less than ‐950 = 11.0%
0
50000
100000
150000
‐1050 ‐950 ‐850 ‐750 ‐650
Lung Histog
CT Number [HU]
g
An Acquisition Specification Table from the Draft Profile
2.2 Radiation Exposure Specifications CT acquisition shall use a tube current –rotation time product such that the pixel standard deviation measured in the lung test object described in IV. Compliance is less than 21 HU and greater than 19 HU.
This is a nominal 50 mAs scan. COPDGene Phantom Data suggests that the specified mAs will range from 72 mAs to 32 mAs.
1. Subject Handling1.1 Contrast Preparation and Administration, no contrast agent1.2 Subject Positioning, e.g., table height (3 DICOM Fields)1.3 Breathing Instructions during Acquisition
2. Image Data Acquisition2.1 Spatial‐Temporal Scanning Specifications, e.g., single slice thickness and pitch
(7 DICOM Fields)
Tables in Image Acquisition Section from the Draft Profile
2.2 Radiation Exposure Specifications (6 DICOM Fields)2.3 Informational Specifications, e.g., vendor model and serial number (4 DICOM
Fields)
3. Image Data Reconstruction , e.g. slice thickness and reconstruction algorithm (6 DICOM Fields)
QIBA Profile Elements for Discussion
1. Specialized QC Phantom – Water phantom is not sufficient
2. Breathing coaching and instructions
3 i l i i l ifi d3. Pixel noise precisely specified
4. Attempted to harmonize acquisition specification with CT air way size and CT lung nodule requirements.
5. Audience issues.
CT-based screening of the chest
Q tifi ti b d l- Quantification beyond lung cancer -
Pim A de Jong, Utrecht, the Netherlands
Take home points
1. Arterial, pulmonary and skeletal diseases can be quantified on
lung cancer screening chest CT
2. Cardiovascular events, lung cancer and osteoporotic events
can be predicted by using these quantitative measures
3. RCT multiple disease screening?
• screening of humans and not lung cancer
Lung cancer Emphysema
Bronchitis
Bronchiolitis
ILD
Problems in humans who smoke …
Systemic
inflammation
Cardio-vascular disease
OsteoporosisMetabolic syndrome
CT X-thoraxCardiovascular 3.37 3.28Lung cancer 2.96 3.51
Mortality in NLST
gOther cancer 2.89 3.08Respiratory 1.21 1.58
NEJM, 2011 Aug 4;365(5):395-409.
per 1000 personyears
Vertebral fracture assessment
Comprehensive CT quantification
osteoporosis
Lung segmentation and densitometry
CT emphysema
IN 1 35%IN-950 = 1.35%
CT Air trapping
E/I ti 0 82E/I-ratioMLD = 0.82
Airway analysis
Fraunhofer MEVIS and DIAG Nijmegen
Cross-section every 1 mm along centerline
On average 1280 measurements per CT
Fraunhofer MEVIS and DIAG Nijmegen
CT bronchial wall thickness
Pi10 = 2.41 mm
Coronary and aortic calcifications
Isgum I, Med Phys 2010; 37(2):714-723
Vertebral fractures and bone density
Buckens CF, JBMR 2013 Jul 2
Why comprehensive CT quantification ?
1. reduce effort and variability
2. multiple disease screening
3. adjust screening intensity to individual risk
PROGNOSTIC VALUE OF ARTERIAL CALCIFICATIONS ON LUNG CANCER
SCREENING CT
Mets OM JACC Cardiovasc Imaging. 2013 Aug;6(8):899-907.
Derivation of a prediction model
1. Single center from the NELSON trial
2. N=1834 males, 60 years, 38 packyears
3. Age, smoking status, packyears, coronary calcium volume, aortic
calcium volume, cardiovascular history
4 From the baseline CT participants were followed for 3 years for CVD4. From the baseline CT participants were followed for 3 years for CVD
events (145 events)
5. Cox regression
6. Bootstrapping + shrinking
7. Internally validated prediction model
Validation of the prediction model
1. Another center from the NELSON trial
2. N=1725 males, 60 years, 38 packyears
3. Followed for 3 years: 118 CVD events
4. Calibration was good
5 Discrimination C index 0 71 (95%CI 0 67 0 76)5. Discrimination C-index 0.71 (95%CI, 0.67 – 0.76)
Hazard Ratios
Coronarycalcium
HR (95%CI)* Aortic calcium
HR (95%CI)*
100 mm3 1.08 (1.05 – 1.11) 100 mm3 1.02 (1.00 – 1.03)
250 mm3 1.21 (1.13 – 1.31) 250 mm3 1.04 (1.01 – 1.07)
500 mm3 1.48 (1.27 – 1.72) 500 mm3 1.08 (1.02 – 1.15)
1000 mm3 2.18 (1.61 – 2.94) 1000 mm3 1.17 (1.04 – 1.33)
≥ 1500 mm3 3.22 (2.05 – 5.05) 1500 mm3 1.27 (1.06 – 1.53)
2500 mm3 1.50 (1.10 – 2.04)
≥ 4000 mm3 1.90 (1.16 – 3.12)
* In multivariate analysis with age, smoking status, smoking history and cardiovascular history
3-years risk calculator CVD risk according to the model
non High-risk (<6%)
High-risk (≥6%)
3-year risk [%], median (P25-P75) 3.4 (2.4 – 4.5) 10.3 (7.7 – 16.3)
Cardiovascular events N (%) 46 (4 0) 72 (12 2)Cardiovascular events, N (%) 46 (4.0) 72 (12.2)
No medication, % 68.3 42.1
Antihypertensive drugs only, % 12.9 15.7
Statins only, % 6.5 5.0
Both, % 12.2 37.2
Osteoporosis measures on lung cancer screening CT scans and all-cancer screening CT scans and all-
cause mortality
Stan Buckens, JBMR 2013 Jul 2
Are vertebral fractures or bone density independent predictors of mortality ?
Case-cohort osteoporosis study in NELSON
• Dual center longitudinal case-cohort study
• 3673 lung cancer screening trial participants
• Cases 196 deaths median follow-up of 6 years
• Random sample of N=394 from the cohort
• Presence and severity of vertebral fractures
• Bone density (Hounsfield Units) in Thoracic 12 or Lumbar 1
• Age, smoking details, coronary and aorta calcification volume
and pulmonary emphysema
• Cox proportional hazards modeling
Osteoporosis and all-cause mortality
Crude Fully adjusted*
fracture yes/no 1.93 (1.37 - 2.73) 2.18 (1.47 - 3.22)
worst fracture grade 1 1.93 (1.33 - 2.80) 2.12 (1.38 - 3.25)worst fracture grade 1 1.93 (1.33 2.80) 2.12 (1.38 3.25)
worst fracture grade 2 -3 1.97 (1.11 - 3.48) 2.37 (1.28 - 4.39)
cumulative fracture grade 1-3 1.89 (1.31 - 2.72) 2.13 (1.40 - 3.24)
cumulative fracture grade ≥4 2.16 (1.38 - 4.09) 2.37 (1.18 - 4.77)
vertebral attenuation 1.087 (1.027 – 1.15) 1.076 (1.006 - 1.15)
Risk for a future hip fracture
Buckens CF, JBMR 2013
Conclusion osteoporotic measures
• Vertebral fractures and bone density are associated with all-
cause mortality
• independent of age, smoking characteristics, coronary and
aortic calcifications and emphysema in male lung cancer
screening participants
Diagnosis of COPD in lung cancer screening CT
Objective
Can low-dose lung cancer screening CT be used to identify participants with h i b t ti l di ?chronic obstructive pulmonary disease ?
Mets et al. (2011) Identification of COPD in lung cancer screening CT scans. JAMA 306(16):1775-1781
Methods
N=1173 Nelson Utrecht, CT/PFT same day
Current or former heavy smokers 50 -75 years; smoking history >16.5 pysy ; g y py
COPD prebronchodil FEV1/FVC < 0.70723 normal lung function283 mild obstruction (FEV1 >80%)167 FEV1 <80%
Study populationMale, n (%) 1140 (97.2)
Age, years 62.1 ± 5.2
BMI, kg/m2 27.0 ± 3.6
Packyears 40 9 ± 17 9Packyears 40.9 ± 17.9
Current smoker, n (%) 629 (53.6)
Former smokers, n (%) 544 (46.4)
FEV1, %predicted 94.7 ± 17.6
FEV1/FVC, % 70.9 ± 9.4
Airflow obstruction, n (%) 450 (38.4)
Performance of chest CT
ACC(%)
SENS(%)
SPEC(%)
PPV(%)
NPV(%)
Baseline model 66.2 28.6 89.6 63.1 66.9
+ CT emphysema78 2 62 7 87 9 76 3 79 1
+ CT air trapping78.2 62.7 87.9 76.3 79.1
+ CT emphysema + CT-AWT
80.6 70.7 86.8 76.9 82.7
+ CT emphysema + CT-AWT + CT air trapping
82.8 73.2 88.8 80.2 84.2
Mets OM et al. Respiratory Research. 2013 May 27;14:59
Performance of CT in symptomatics
C-index (95% CI)
Asymptomatics Symptomatics
Baseline model 0 674 (0 625 0 722) 0 634 (0 589 -Baseline model 0.674 (0.625 -0.722) 0.634 (0.589 -
0.679)
+ CT emp BWT 0.828 (0.790 – 0.866) 0.886 (0.859 -
0.912)
+CT emp BWT Air T 0.832 (0.795 – 0.869) 0.905 (0.881 -
0.929)
Comprehensive CT quantification in lung cancer screening
1. Aiding detection of pulmonary nodules (first reader?)2. Quantification volume/mass pulmonary nodules3. Prediction of cardiovascular events4. Prediction osteoporotic fractures5. Diagnosis of COPD6. Prediction of lung cancer risk
Quantification of Pulmonary Vasculature on CT
Raúl San José Estépar1, Ph.D.
Department of Radiology, Brigham and Women’s Hospital, Boston MA
Disclosures
• No disclosures
BackgroundPulmonary Vascular Disease
• Estimated prevalence of disease 26–70%1,2
• Presence associated with – Increased health care utilization3
– Increased mortality4
• Pathologic changes seen even in smokers with normal lung functionfunction
1. NEJM 1972;286:912-8
2. AJRCCM 2002;166:314-22
3. AJRCCM 1999;159:158-64
4. Am Rev Resp Dis 1979;119:895-902
Three Key Players in Vascular Related Smoking Pathology
Vessel RemodelingVessel Remodeling
InflammationInflammation
Changes are seen in Changes are seen in ““healthyhealthy”” heavy heavy
smokers NOT just those with smokers NOT just those with
quantifiable airways obstructionquantifiable airways obstruction
Endothelial Endothelial dysfunctiondysfunction
Imaging Landscape for Assessment of Pulmonary Vascular Disease
Static Vessel
Morphology
Perfusion
Adaptive VQ
Matching
Other Imaging
Phenotypes
Genetics, Biomarkers
Mathematical Models: Enhance Value of Imaging
Biomarker Personalized Disease Model Incorporating Pulmonary
Vascular Network Dynamics
Why pulmonary vascular morphology?
• Static assessment of V/Q
• Potentially more sensitive marker of disease than airways or parenchyma biomarkers
• Evolution of central vasculature morphology• Evolution of central vasculature morphology that has shown to be highly statistically significant predictor of AECOPD1
• Largely unexplored
1 Wells et al. NEJM, 2012
Slide provided by Philippe Grenier
Prior Work
• Vascular pruning has been observed during angiography and histologic analysis in patients with PAH
• Less than a handful of studies using imaging to tif th d f h i h lquantify the degree of changes in morphology
of pulmonary vasculature
Matsuoka et al, Am J Respir Crit Care Med. 2010;181(3):218-225
Fractal Dimension
Moledina et al, Heart 2011 Aug;97(15):1245-9
CT Assessment of Vascular MorphologyCT Scan Lung and lobe extraction
Vessel Enhancing FilterScale Space Particles
Methods
• BV(CSA): Blood volume distribution as a function of CSA
• p(CSA): CSA probability• p(CSA): CSA probability density function – Kernel density estimation from
particle points
• Δ = Inter‐particle spacing
– Δ =0.42 mm
• CSA = pi * 2( σ2 + σ20)
CT Assessment of Vascular Morphology
San Jose Estepar et al. AJRCCM, 2013
Patient Population
COPDGeneCohort
(n=10,362)
COPDGeneCohort
(n=10,362)
First 2500 subjectsFirst 2500 subjectssubjectssubjects
NJH(n=359) NJH
(n=359)
Never smokers Controls(n=82)
Never smokers Controls(n=82)
GOLD 0(n=104) GOLD 0(n=104)
GOLD 1(n=27) GOLD 1(n=27)
GOLD 2(n=90) GOLD 2(n=90)
GOLD 3(n=87) GOLD 3(n=87)
GOLD 4(n=51) GOLD 4(n=51)
Population Characteristics
Never‐Smoking Controls (n=85)
Smokers (n=359) P Value
Age 62.4 (56.3‐69.1) 63.7 (54.6 – 70.1) NS
FEV1pp 103.7 (93‐113) 59 (38 – 89) <0.0001
Height (cm) 163.2 (159.5‐171.6) 168.7 (163.0 – 176.1) NS
Weight (kg) 80.2 (63.8‐88.5) 78.6 (64.3 – 90.0) NS
BMI 27 5 (24 1 31 4) 26 65 (23 08 30 70) NSBMI 27.5 (24.1‐31.4) 26.65 (23.08 – 30.70) NS
%LAA‐950 0.8 (0.4 – 2.3) 6.33 (1.75 – 22.76) <0.0001
SO2 NA 93 (90 – 95)
6 MWD (ft) NA 1310 (1025 – 1650) SGRQ TotalScore
NA 32.16 (15.94 – 47.26)
BODE (n=352) NA 2 (0‐4)
DLCO (n=135) NA 13.11 (8.58 – 16.64)
CT ProtocolScanner ConfigurationScanner Make GE Siemens
Scanner ModelLight
Speed 16Discovery HD750
DefinitionDefinition
AS+Definition Flash
Scan Type Helical Helical Spiral Spiral Spiral
Rotation Time (s) 0.5 0.5 0.5 0.5 0.5
Det. Configuration 16x0.625 64x0.625 64x0.6 128x0.6 128x0.6
Pi h 1 375 1 375 1 1 1 1Pitch 1.375 1.375 1.1 1 1Speed (mm/rot) 13.75 13.75 13.2 38.4 38.4kVp 120 120 120 120 120
mAs 200 200 200 200 200Reconstruction
Algorithm Standard Standard B31f B31f B31f
Thickness (mm) 0.625 0.625 0.75 0.75 0.75Interval (mm) 0.625 0.625 0.5 0.5 0.5
SubjectsSmokers 42 317Non‐smokers 12 5 36 5 24
Blood Volume differences between Normal and Smokers
Dilation in the range 7-50 mm2
and pruning in less than 5 mm2G
OLD
0-2
GO
LD 3-4
BVCOPD < BVNormal
BVCOPD > BVNormal
Pruning
Dilation
Associations in Smokers
San Jose Estepar et al. AJRCCM, 2013
Therapy Assessment (I): ELVR
Slide provided by F. Rahahi
Endoscopic Lung Volume Reduction
ELVR Endoscopic Lung Volume Reduction
25000
27000
29000
31000
33000
35000
30000
35000
40000
45000
14 patients in the prior Aeriseal trial with CT scans prior to ELVR, 12 weeks and 48 weeks.
15000
17000
19000
21000
23000
1 2 3
15000
20000
25000
1 2Week 0 Week 0Week 12 Week 12 Week 48
• +12% change between week 0 and week 12 (N = 13) and 22% change between week 0 and week 48 (N = 5). Consistent with hypothesis of decompression.
F Rahaghi, to be presented at ATS
Therapy Assessment (II): Nitric Oxide Challenge Nitric Oxide Challenge
Right Lung BV5mm2 BVTOT BV5mm2/BVTOT BV60‐90mm2
Baseline 7.63E+04 1.59E+05 47.87% 1.06E+04
+Nitric 8.41E+04 1.62E+05 51.82% 9.99E+03
What else can Vascular QCT offer?
Bronchiectasis• Assessment of Airway to Vessel Ratio (A/V) by matching
closest points• Compute A/V distribution over vessel cross sectional area
(CSA)
BronchiectaticNon-Bronchiectatic
• Early analyses show differences with between GOLD U and GOLD 0‐4 and being a independent prediction with severe exacerbations
> 10 mm2
> 10 mm2
Conclusions
• Pulmonary vascular morphology in smokers appears to be characterized by distal pruning and proximal dilation
• There is a discrete point in vessel size that characterizes that transition and can be detected by CT
• Difference are more significant in proximal vessels
• Future studies are necessary to fully characterize this transition point in COPD and other diseases
– Use for epidemologic investigations and therapeutic outcomes.
THANK YOU
Quantitative CT in lung fibrosis
David A Lynch, MBDavid A Lynch, MB
Quantification of lung fibrosisQuantification of lung fibrosis
SemiquantitativeSemiquantitative Densitometry/CT histogramDensitometry/CT histogram
TextureTexture based methodsbased methods TextureTexture--based methodsbased methods
SemiquantitativeSemiquantitative assessment in assessment in IPFIPF
Extent of fibrotic abnormalityExtent of fibrotic abnormality Extent of ground glass abnormalityExtent of ground glass abnormality
Extent of emphysemaExtent of emphysema Extent of emphysemaExtent of emphysema 55--point scale: 0, 1point scale: 0, 1--25%, 2625%, 26--50%...50%... 1111--point scale: 0, 1point scale: 0, 1--10%, 1110%, 11--20%...20%... 2121--point scale: 0, 5%, 10%.....point scale: 0, 5%, 10%.....
Relationship between Relationship between semiquantitativesemiquantitativeassessment and physiologic impairmentassessment and physiologic impairment
Extent of IPF on CT was independently Extent of IPF on CT was independently related to:related to: % predicted DLCO r= 0.65, p <0.0005% predicted DLCO r= 0.65, p <0.0005 % predicted FVC% predicted FVC r= r= --0.53, 0.53, p <p <0.00050.0005 % predicted % predicted FEV1FEV1 r= r= --0.34, 0.34, p <p <0.020.02
Composite physiologic index:Composite physiologic index: extent of disease on CT = 91.0extent of disease on CT = 91.0-- (0.65 X (0.65 X
DLCO%)DLCO%)-- (0.53 X FVC%)+ (0.34 X FEV1(0.53 X FVC%)+ (0.34 X FEV1%)%)
Wells et al. Am J Respir Crit Care Med Vol 167. pp 962–969, 2003
Relationship between Relationship between semiquantitativesemiquantitative assessment and assessment and
physiologic impairmentphysiologic impairment
Wells et al. Am J Respir Crit Care Med Vol 167. pp 962–969, 2003
Relationship between semiquantitative assessment and mortality: Univariate
Baseline Variable Hazard ratio 95% Confidence Interval
p Value
Overall fibrosis extent
3.12 2.00, 4.89 < 0.0001
Reticulation pattern extent
2.69 1.71,4.23 < 0.0001
Honeycomb pattern extent
3.06 1.75, 5.34 < 0.0001extentPredominant pattern: reticulation
0.41 0.17, 0.99 0.04
Percent-predicted DLCO
0.92 0.89, 0.96 0.0001
A-a gradient 1.06 1.03, 1.09 < 0.0001Current oxygen use 2.37 1.29, 4.34 0.004
Percent-predicted FVC
0.97 0.94, 1.00 0.03
Lynch DA, et al. Am J Respir Crit Care Med2005;172:488-93.
Relationship between semiquantitative assessment and mortality: Multivariate
Baseline Variable
Hazard ratio 95% Confidence Interval
p Value
HRCT features
Overall extent 2 71 1 61 4 55 0 0001Overall extent of fibrosis
2.71 1.61, 4.55 < 0.0001
% predicted DLCO
0.94 0.90, 0.98 0.004Treatment assignment to IFN-γ1b
0.53 0.28, 0.99 0.04
Lynch DA, et al. Am J Respir Crit Care Med2005;172:488-93.
Scleroderma lung studyScleroderma lung study
N= 158N= 158 Randomized cyclophosphamide Randomized cyclophosphamide vsvs placeboplacebo Mean absolute difference in FVC at 12 Mean absolute difference in FVC at 12
months= 2.53% (0.28, 4.9%), favoring months= 2.53% (0.28, 4.9%), favoring cyclophosphamide (p<0.03)cyclophosphamide (p<0.03)
When adjusted for worst zonal When adjusted for worst zonal semiquantitativesemiquantitative score for fibrosis on baseline score for fibrosis on baseline CT, mean absolute difference increased to CT, mean absolute difference increased to 2.97% (0.75, 5.19%), (P=0.0092.97% (0.75, 5.19%), (P=0.009))Tashkin et al. N Engl J Med 2006; 354:2655-2666
Limitations of Limitations of semiquantitativesemiquantitativescoringscoring
SubjectiveSubjective InterInter--observer variationobserver variation
Relative lack of precision for longitudinalRelative lack of precision for longitudinal Relative lack of precision for longitudinal Relative lack of precision for longitudinal analysisanalysis
CTCT--based quantification of lung based quantification of lung fibrosisfibrosis
Densitometry/CT histogramDensitometry/CT histogram Local histogram methodsLocal histogram methods
TextureTexture based methodsbased methods TextureTexture--based methodsbased methods
HistogramHistogram--based parametersbased parameters
Best et al., Radiology; 2003;208:407-414
HistogramHistogram--based parametersbased parameters
Mean lung attenuation Skewness
Degree of leftward or rightward deviation ofrightward deviation of histogram, compared with Gaussian
Kurtosis Degree of peakedness of
histogram, compared with Gaussian
Best et al., Radiology; 2003;208:407-414
Relationship between quantitative Relationship between quantitative histogram assessment and histogram assessment and
physiologic impairmentphysiologic impairment
R2= 0.23P<0.0001
R2= 0.30P<0.0001
R2= 0.20P<0.0001
Best et al., Radiology; 2003;208:407-414
UnivariateUnivariate analysis of predictors of analysis of predictors of mortality in IPF (n=167, 35 deaths)mortality in IPF (n=167, 35 deaths)
Baseline parameters Odds ratio estimates
95% Confidence Limits
Wald Chi-Square Pr > ChiSq
FVC (% predicted) 0.976 0.949 to 1.004 2.7323 0.0983
TLC (% predicted) 0.97 0.942 to 0.998 4.3191 0.0377
MLA (HU) 1 004 0 998 t 1 011 1 533 0 2157MLA (HU) 1.004 0.998 to 1.011 1.533 0.2157
Skewness 0.29 0.092 to 0.915 4.4584 0.0347
Kurtosis 0.503 0.287 to 0.883 5.7355 0.0166
CT extent of fibrosis (%) 1.125 1.041 to 1.217 8.714 0.0029
CT extent of GGO (%) 0.992 0.949 to 1.037 0.1328 0.7155
CT extent of emphysema (%) 0.986 0.894 to 1.088 0.0797 0.7777
Treatment assignment 0.86 0.311 to 2.378 0.0843 0.7716
Best et al., Radiology; 2008 Mar;246(3):935-40.
Multivariate analysis of predictors of Multivariate analysis of predictors of mortality in IPF (n=167, 35 deaths)mortality in IPF (n=167, 35 deaths)
Effect
Odds Ratio Estimates
95% Confidence Limits
Wald Chi-Square
Pr > ChiSq
Kurtosis at 0.32 to Baseline 0.579 1.049 3.249 0.0715
Mean Fibrosis at Baseline 1.104
1.018 to 1.198 5.7171 0.0168
Best et al., Radiology; 2008 Mar;246(3):935-40.
Changes in CT parameters over 12 Changes in CT parameters over 12 months (n=95)months (n=95)
Baseline 12 Months P value
Mean (SD) Mean (SD)
Mean lung attenuation (HU) -707.4 (60.8) -691.5 (62.4) 0.0032(HU) 707.4 (60.8) 691.5 (62.4) 0.0032
Skewness 1.043 (0.392) 0.911 (0.42) <0.0001
Kurtosis 0.497 (1.065) 0.204 (1.0) <0.0001
SemiquantitativeAssessment of fibrosis (%) 17.7 (5.4) 18.5 (6.0) 0.0302
Relationship between quantitative Relationship between quantitative and and semiquantitativesemiquantitative assessment assessment
and physiologic impairmentand physiologic impairment
Sverzellati et al. Radiol med (2007) 112:1160–1172
Sources of variation in histogram Sources of variation in histogram based analysisbased analysis
Inspiratory lung volumeInspiratory lung volume Careful instruction in breathing is mandatoryCareful instruction in breathing is mandatory
Associated emphysema Associated emphysema p yp y Technical factorsTechnical factors
CT doseCT dose Scanner reconstruction parametersScanner reconstruction parameters Scanner make/modelScanner make/model
Segmentation and analysis platformSegmentation and analysis platform
Local histogram analysisLocal histogram analysis
Iwasawa et al J Thorac Imag 2009: 24: 216-222
Local histogram analysisLocal histogram analysis
Iwasawa et al J Thorac Imag 2009: 24: 216-222
Local histogram analysis Local histogram analysis vsvssurvivalsurvival
Iwasawa et al J Thorac Imag 2009: 24: 216-222
Dissimilarity assessment of Dissimilarity assessment of cumulative density functionscumulative density functions
Bartholmai et al. Journal of Thoracic Imaging. 2013;28(5):298-307
GlyphGlyph--based characterization of based characterization of lung parenchymalung parenchyma
Bartholmai et al. Journal of Thoracic Imaging. 2013;28(5):298-307
Texture based methodsTexture based methods
Adaptive multiple feature methodAdaptive multiple feature method Quantitative lung fibrosis scoreQuantitative lung fibrosis score
Adaptive multiple feature methodAdaptive multiple feature methodAMFMAMFM
Includes Includes FirstFirst--order histogram featuresorder histogram features RunRun--length, and colength, and co--occurrence featuresoccurrence features Fractal featuresFractal features Fractal featuresFractal features
Identify and visually characterize features of Identify and visually characterize features of interest (ground glass, reticular, honeycombing, interest (ground glass, reticular, honeycombing, etc.)etc.)
Optimal feature selection using Bayesian Optimal feature selection using Bayesian classificationclassification
Apply classification to test setApply classification to test setXu Y, et al. Acad Radiol. 2006;13(8):969-78.
Adaptive multiple feature Adaptive multiple feature methodmethod
Honey-comb
GGO Broncho-vascular
Nodular Emphy-sema
Normal
Sensitivity 82.5 94.3 89.2 86.7 99.2 92.7Sensitivity (%)
82.5 94.3 89.2 86.7 99.2 92.7
Specificity (%)
99.9 99.1 98.9 96.6 100 97.8
Uppaluri R et al. Am J Respir Crit Care Med. 1999 Aug;160(2):648-54.
Adaptive multiple feature method:Adaptive multiple feature method:2d2d--3d3d
AMFM Whole lung classificationAMFM Whole lung classification
Honeycomb
Ground Glass –Reticular
B h l
Ground Glass
Bronchovascular
Normal
Emphysema
Flaherty, ATS 2013
Quantitative lung fibrosis (QLF) Quantitative lung fibrosis (QLF) scorescore
Kim et al. Clin Exp Rheumatol. 2010 ; 28(5 Suppl 62): S26–S35.
Quantitative lung fibrosis (QLF) Quantitative lung fibrosis (QLF) scorescore
Kim et al. Clin Exp Rheumatol. 2010 ; 28(5 Suppl 62): S26–S35.
Scleroderma: sequential Scleroderma: sequential evaluationevaluation
Kim et al, Eur Radiol. 2011 Dec;21(12):2455-65
Scleroderma: sequential Scleroderma: sequential evaluationevaluation
Kim et al, Eur Radiol. 2011 Dec;21(12):2455-65
Textural analysis at NJH: Textural analysis at NJH: TrainingTraining
Expert selection of representative ROIs (confirmed by Expert selection of representative ROIs (confirmed by 2nd) demonstrating characteristic patterns (2nd) demonstrating characteristic patterns (normal normal and and IPF) in cases drawn from established IPF) in cases drawn from established IPFNetIPFNet studystudy
Retain full DICOM metadata to control imaging Retain full DICOM metadata to control imaging parametersparameters
Feature extractionFeature extraction
•• 15 x 15 pixel ROIs from radiologist drawn patches15 x 15 pixel ROIs from radiologist drawn patches•• Compute variety of local features:Compute variety of local features:
-- Kernel Density Estimate (KDE) of local intensity histogramKernel Density Estimate (KDE) of local intensity histogram-- Local Binary Pattern (LBP)Local Binary Pattern (LBP)
Hi t f O i t d G di t (HOG)Hi t f O i t d G di t (HOG)-- Histogram of Oriented Gradients (HOG)Histogram of Oriented Gradients (HOG)-- Scale Invariant Feature Transform (SIFT)Scale Invariant Feature Transform (SIFT)
ROI Label
Fibrotic Non-fibrotic
Classifier prediction
Fibrotic 11257 202
Non fibrotic 179 3026
Textural analysis at NJH: results summary
Non-fibrotic 179 3026
Sensitivity 11257 / (11257+179) 98.4%
Specificity 3026 / (3026+202) 93.7%
Pos.predicted 11257 / (11257+202) 98.2%
Neg.predicted 3026 / (179+3026) 94.4%
NJH: Textural analysisNormal lung Reticular
Abnormality
NJH: Textural analysisNJH: Textural analysisNormal lung
Reticular Abnormality
Advantages of imageAdvantages of image--based based quantification in IPFquantification in IPF
Provides morphologic characterizationProvides morphologic characterization ReticularReticular HoneycombHoneycomb Ground glassGround glass
I l d f i l i f iI l d f i l i f i Includes functional informationIncludes functional information Lung volumesLung volumes
Provides regional and lobar informationProvides regional and lobar information Sensitive, and increasingly sophisticated, quantitative Sensitive, and increasingly sophisticated, quantitative
tools are availabletools are available Emerging information on reproducibility, longitudinal Emerging information on reproducibility, longitudinal
change and normal valueschange and normal values
Textural analysis in IPF: limitationsTextural analysis in IPF: limitations
Sources of variationSources of variation Level of inspirationLevel of inspiration Scanner make/model/reconstruction algorithmScanner make/model/reconstruction algorithm Scanner make/model/reconstruction algorithmScanner make/model/reconstruction algorithm CT doseCT dose Analysis techniqueAnalysis technique
Radiation doseRadiation dose More validation is neededMore validation is needed
SummarySummary
Textural quantitative imaging has Textural quantitative imaging has substantial potential value for substantial potential value for quantification and characterization of quantification and characterization of qqsarcoid and IPFsarcoid and IPF
2014.03.15. Society of Thoracic Radiology 2014 QCT Imaging of the Lung (San Antonio, TX, USA)
MRI of the Lung
Yoshiharu Ohno, M.D., Ph.D. 1, 2 1 Advanced Biomedical Imaging Research Center, Kobe University Graduate
School of Medicine 2Division of Functional Imaging Research, Department of Radiology, Kobe
University School of Medicine
Objective
1. To learn the state-of-the art lung MR imaging techniques
for various chest disease.
2. To understand the potential of quantitatively assessed
MR imaging for various chest diseases.
3. To learn the clinical utility of quantitative and semi-
quantitative assessment of lung MR imaging for not only
diagnosis, but also patients’ management and treatment
response in various pulmonary diseases.
Aims for Quantitative Assessment on Lung MRI
1. Physiology and Physiopathology Assessment in normal and Pulmonary Disease Subjects
2. Diagnosis
• Pulmonary Nodule and Mediastinal Tumor
• N-Staging of Lung Cancer
3. Disease Severity Assessment
• Pulmonary Vascular Diseases
• Airway and Lung Parenchyma Diseases
• Thoracic Malignancies
4. Patients’ Care and/ or Cure
• Thoracic Malignancies
• Pulmonary Vascular Diseases
• Airway and Lung Parenchyma Diseases
Quantitative Methods for Lung MRI
1. Oncology:
• STIR Turbo SE Imaging
• Diffusion-Weighted MR Imaging
• Dynamic Contrast-Enhanced First-Pass Perfusion MR Imaging
• Oxygen-Enhanced MR Imaging
2. Pulmonary Vascular Diseases
• Dynamic Contrast-Enhanced First-Pass Perfusion MR Imaging
• Phase-Contrast MR Imaging
• Hyper-Polarized Noble Gas MR Imaging (and Oxygen-Enhanced
MR Imaging)
3. Airway and Lung Parenchyma Diseases
• Hyperpolarized Noble Gas MR Imaging
• Oxygen-Enhanced MR Imaging
• Dynamic Contrast-Enhanced First-Pass Perfusion MR Imaging
• Pulmonary MR Imaging with Ultra-Short TEs
STIR Turbo SE Imaging
75-year-old male patient with metastatic LNs from invasive Ad.
CE-CT STIR turbo SE Imaging PET/CT
67-year-old female patient with non-metastatic LN from invasive Ad.
CE-CT STIR turbo SE Imaging PET/CT
STIR Turbo SE Imaging vs. CE-CT
Contrast- Quantitatively assessed Qualitatively assessed enhanced CT STIR images STIR images
Sensitivity (%) 53 93* 88*
(21/40) (37/40) (35/40)
Specificity (%) 83 87 86
(58/70) (61/70) (60/70)
Accuracy (%) 72 89* 86*
(79/110) (98/110) (95/110)
Ohno Y, et al. Radiology. 2004; 231: 872-879.
STIR Turbo SE Imaging vs. FDG-PET/CT
Modality Index Feasible TV SE (%)
90.1
SP (%)
93.1
AC (%)
92.2
STIR turbo SE imaging
LSR
0.6
(39/43)
(67/72)
(106/115)
Co-registered FDG-PET/CT
SUV
2.0
76.7*
(33/43)
87.5
(63/72)
83.5*
(96/115)
TV: Threshold value, SE: Sensitivity, SP: Specificity, AC: Accuracy
LSR: Lymph node-salilne ratio
*: Signifiacant difference with LSR (p<0.05)
Ohno Y, et al. J Magn Reson Imaging. 2007; 26: 1071-1780.
Diffusion-Weighted MR Imaging (DWI)
75-year-old female patient with invasive adenocarcinoma (N1)
Thin-Section CT STIR turbo SE Imaging DWI
73-year-old female patient with invasive adenocarcinoma (N1)
Thin-Section CT STIR turbo SE Imaging DWI PET/CT
Diffusion-Weighted MR Imaging (DWI)
SE (%) SP (%) AC (%)
STIR turbo SE imaging
LSR
LMR
82.7 89.2 86.8
(77/93) (140/157) (217/250)
82.7 89.2 86.8
(77/93) (140/157) (217/250)
DWI ADC (×10-3s/mm2) 74.2*, ** 90.4 84.4*, **
(69/93) (142/157) (211/250)
FDG-PET/CT SUVmax
74.2*, ** 94.9 85.6
(69/93) (145/157) (214/250) LSR: Lymph node-salilne ratio, LMR: Lymph node-muscle ratio *: Signifiacant difference with LSR (p<0.05) **: Signifiacant difference with LMR (p<0.05)
Ohno Y, et al. Radiology. 2011; 261: 605-615.
N-Staging in Non-Small Cell Lung Cancer
-STIR Turbo SE Imaging and DWI-
• Quantitatively assessed STIR turbo SE imaging has better
capability for diagnosis of lymph node metastasis than contrast-
enhanced CT, FDG-PET or PET/CT and DWI in non-small cell lung
cancer (NSCLC) patients.
• Quantitative Assessment of DWI using ADC has equal capability
for N-staging in NSCLC patients as compared with FDG-PET/CT.
• Further DWI sequence improvement and analysis software
development will provide better potential, and encourage this
technique in routine clinical practice.
Ohno Y, et al. Radiology. 2004; 231: 872-879. Ohno Y, et al. J Magn Reson Imaging. 2007; 26: 1071-1780. Ohno Y, et al. Radiology. 2011; 261: 605-615.
Dynamic MR Imaging for Nodule Management
-SE vs. Ultra-Fast GRE (or Fast GRE)-
SE (or Turbo SE) technique Ultra f-GRE (or f-GRE) technique
t=0s t=150s t=8.8s t=9.9s t=15.4s
Adenocarcioma Adenocarcioma
t=0s t=150s t=0s t=12.1s t=23.1s
Tuberculoma
Tuberculoma
Dynamic CT vs. PET/CT vs. Dynamic MRI -Differentiation of Malignant Nodules from Benign Nodules-
Maximum enhancement combined with
absolute loss of enhancement
Net enhancement combined with absolute
loss of enhancement
Cut-off value SE (%) SP (%) AC (%)
93.4 42.0 a, b, c 80.7 a, b, c 60.0HU and ≤30HU
(142/152) (21/50) (163/202)
94.7 52.0b 83.2b 20.0 HU and ≤30 HU
(142/152) (26/50) (168/202)
Slope of enhancement combined with
absolute loss of enhancement
0.4HU/sec and
≤30HU
94.7 48.0b 82.2a, b
(142/152) (24/50) (166/202)
96.0 54.0 85.6 Maximum relative enhancement ratio 0.2
Slope of enhancement ratio 0.04/sec
SUVmax 1.8
(146/152) (27/50) (173/202)
96.0.0 64.0 88.1 (146/152) (32/50) (178/202)
93.4 54.0 83.6b
(142/152) (27/50) (169/202)
SE: Sensitivity, SP: Specificity, AC: Accuracy a: Significant difference from maximum relative enhancement ratio (p<0.05). b: Significant difference from slope of enhancement ratio (p<0.05). c: Significant difference from SUVmax (p<0.05).
Ohno Y, et al. J Magn Reson Imaging. 2008; 27: 1284-1295.
Dynamic MR Imaging for Nodule Management
-SE vs. Ultra-Fast GRE (or Fast GRE)-
• Dynamic CE-MR imaging has equal potential for distinguishing
malignant from benign nodules, when compared with dynamic CT,
FDG-PET and 99mTc-depreotide SPECT.
• Dynamic CE-MR imaging has performed by various sequences and
techniques, and drawbacks are time consuming or low spatial
resolution.
• Quantitative software as well as sequence improvements and
technical reproducibility will encourage this technique in routine
clinical practice.
Ohno Y, et al.Radiology. 2002; 224: 503-511. Ohno Y, et al. J Magn Reson Imaging. 2008; 27: 1284-1295. Cronin P, et al. Radiology. 2008; 246: 772-782.
Quantitatively Assessed Regional Perfusion Parameters using Dynamic
CE-First Pass Perfusion MRI
Pulmonary Blood Flow (PBF) Map
Pulmonary Blood Volume (PBV) Map
Mean Transit Time (MTT) Map
63-year-old male subject with adenocarcinoma in the left upper lobe
Quantitative CT
Perfusion SPECT
Dynamic perfusion MRI
Ohno Y, et al.AJR Am J Roentgenol. 2007; 189: 400-408.
Prediction of Postoperative Lung Function
-Dynamic perfusion MRI vs. Quantitative and Qualitative CT vs. Perfusion SPECT-
The limits of agreement = 5.3±11.8 % The limits of agreement = 5.0±11.6 %
The limits of agreement = 6.8±14.4 % The limits of agreement = 5.1±14.0 %
Ohno Y, et al.AJR Am J Roentgenol. 2007; 189: 400-408.
66-year-old male with acute pulmonary embolism.
Contrast-enhanced MDCT
Time-Resolved CE-MR Angiography
PBF map generated from dynamic CE-perfusion MRI
Ohno Y, et al. J Magn Reson Imaging. 2010; 31: 1081-1090.
Capability for Distinguishing Death from Survival Groups in ACPTE Patients
-CE-CT vs. Time-Resolved CE-MRA vs. CE-Perfusion MRI-
RV/LV diameter ratio PECT index PEMRA index PEPerfusion MRI index
Az 0.96 0.86*, ** 0.86*, ** 0.96
Feasible threshold value 1.6≤ 60% ≤ 60% ≤ 60% ≤
62.5 62.5 62.5 62.5 Sensitivity (%)
(5/8) (5/8) (5/8) (5/8)
Specificity (%) 97.6 83.3*, ** 83.3*, ** 97.6
(41/42) (35/42) (35/42) (41/42)
Accuracy (%) 92.0 80.0 80.0 92.0
(46/50) (40/50)*, ** (40/50)*,** (46/50)
AZ: Area under the curve RV: right ventricle; LV: left ventricle *: Significant difference with RV/LV diameter ratio (p<0.05). **: Significant difference with PEPerfusion MRI index (p<0.05).
Ohno Y, et al. J Magn Reson Imaging. 2010; 31: 1081-1090.
Capability as Survival Predictor in ACPTE Patients
-CE-CT vs. Time-Resolved CE-MRA vs. CE-Perfusion MRI-
Odds ratio* 95% CI p value
RV/LV diameter ratio
1.17
1.05-1.31 0.004
PECT index
1.10
1.03-1.18 0.004
PEMRA index
1.16
1.05-1.27 0.004
PEPerfusion MRI index
1.21
1.06-1.37 0.004
Ohno Y, et al. J Magn Reson Imaging. 2010; 31: 1081-1090.
Quantitative CE-Perfusion MRI 42-year old female primary pulmonary hypertension (PPH) patient
Quantitative PBF Quantitative PBV MTT
Ohno Y, et al. AJR Am J Roentgenol. 2007; 188: 48-56
Quantitative Dynamic First-Pass CE-Perfusion MRI -Disease Severity Assessments in PAH-
r=-0.79, r2=0.62, p<0.001
r=0.60, r2=0.36, p=0.022
Ohno Y, et al. AJR Am J Roentgenol. 2007; 188: 48-56 Ohno Y, et al. J Magn Reson Imaging. 2012; 36: 612-623.
76-year-old male with chronic thromboembolic pulmonary hypertension treated with conservative therapy and assessed as responder
Ohno Y, et al. J Magn Reson Imaging. 2012; 36: 612-623.
Comparison of Physiological, CT- and MR-based indexes between CPTEH patients assessed as
responder and non-responders -CE-CT vs. Time-resolved MRA vs. Perfusion MRI-
Responder group (n=13)
Mean+/-SD
0.2±0. 1
86.5±52.9
-9.9±10.5
-192.8±161.2
-0.3±0.1
-13.1±8.7
-17.8±10.4
33.4±18.2
4.5±2.7
-1.3±1.3
Non-responder group (n=11)
Mean+/-SD
-0.1±0.2*
-16.0±71.3*
2.8±6.5*
31.7±63.5*
0.2±0.3*
0.7±3.7*
0.3±4.2*
0.6±8.8*
0.3±1.9*
0.1±0.5*
P value
Ohno Y, et al. J Magn Reson Imaging. 2012; 36: 612-623.
Improvement in:
CI (l/min/mm2) <0.0001
6-MWD (m) 0.0006
mPAP (mmHg) 0.0023
PVR (dyne/s-1/cm-5) 0.0003
RV/LV diameter ratio
<0.0001
CTEPHCTA (%)
<0.0001
CTEPHMRA (%) <0.0001
PBF (ml/100ml/min) <0.0001
PBV (ml/100ml) 0.0004
MTT (sec) 0.0035 *: Significant difference with responder group (p<0.05).
Ohno Y, et al. J Magn Reson Imaging. 2012; 36: 612-623.
SE (%)
100
SP (%)
81.8
AC (%)
91.7
(13/13) (9/11) (22/24)
100 36.4 70.8
(13/13) (4/11) (17/24)
100 54.5 79.2
(13/13) (6/11) (19/24)
100 90.9* 95.8*
(13/13) (10/11) (23/24)
100 72.7 87.5
(13/13) (8/11) (21/24)
100 72.7 87.5
(13/13) (8/11) (21/24)
Comparison of CT- and MR-based indexes between CPTEH patients assessed as responder and non-responders
-CE-CT vs. Time-resolved MRA vs. Perfusion MRI-
Improvement in: Feasible threshold value
RV/LV diameter ratio ≤0
CTEPHCTA (%) ≤0
CTEPHMRA (%) ≤0
PBF (ml/100ml/min) 7≤
PBV (ml/100ml) 0.9<
MTT (s) <1
*: Significant difference with CTEPHCTA (p<0.05).
Quantitatively Assessed Dynamic CE-First- Pass Perfusion MRI for Chest Disease
• Dynamic CE-first-pass perfusion MR imaging is able to assess regional perfusion changes due to pulmonary emphysema and predict postoperative lung function as well as thin-section CT and perfusion SPECT in NSCLC patients.
• Dynamic CE-first-pass perfusion MR imaging has the potential to assess physiopathology and disease severity in PVD patient as non- invasive method as compared with right heart catheterization.
• Dynamic CE-first-pass perfusion MR imaging can be play as one of the biomarkers for treatment and management as well as one of the predictors for patient outcome in PVD patients.
• Quantitative software as well as sequence improvements and technical reproducibility will encourage this technique in routine clinical practice.
Ohno Y, et al. J Magn Reson Imaging. 2004;20: 353-65. Ohno Y, et al. AJR Am J Roentgenol. 2007; 188: 48-56. Ohno Y, et al. J Magn Reson Imaging. 2010; 31(5): 1081-1090. Ohno Y, et al. J Magn Reson Imaging. 2012; 36: 612-623.
Phase-Contrast MRI
Time-velocity course curves of normal volunteer and patient with high pulmonary
vascular resistance (PVR)
Peak velocity (PV)
Acceleration time (AT)
Nogami M, et al. J Magn Reson Imaging. 2009 ; 30: 973-980. .
Measurement Error Evaluation of SV and PASP -Cardiac US vs. Phase-Contrast MRI-
Between PC-MRI
and catheterization
Between cardiac US
and catheterization
SV [ml]
(n=11)
Mean difference 1.0 9.0
(95%CI of mean difference) (-1.4 ~ 8.4) (-6.0 ~ 56.6)
Upper limits of agreement 7.7 52.2
(95% CI of upper limits of agreement) (3.7 ~ 11.8) (26.2 ~ 78.3)
Lower limits of agreement -5.8 -34.1
(95 %CI of lower limits of agreement) (-9.8 ~ -1.7) (-60.2 ~ -8.1)
Mean difference -3.0 -6.5
PASP
[mmHg]
(n=11)
(95%CI of mean difference) (-7.6 ~ 11.8) (-16.5 ~ 25.0)
Upper limits of agreement 10.5 22.2
(95% CI of upper limits of agreement) (2.3 ~ 18.6) (4.9 ~ 39.5)
Lower limits of agreement -16.5 -35.2
(95 %CI of lower limits of agreement) (-24.6 ~ -8.3) (-52.5 ~ -17.9)
Alunni JP, et al. Eur Radiol. 2010; 20: 1149-1159.
PAH Assessment on Phase-Contrast MRI
-Capability for Disease Severity Assessments-
PH grades
Grade I
Grade II
Grade III
Grade IV
p value
RV Ejection Fraction
36.5
32
21*
13**
0.0005
RV Ejection Systolic Volume
71.5
66.9
119.6*
141.7**
0.0059
RV Area Change
31
33.5
19.5*
10.5**
0.0010
Systolic RV/LV Area Ratio
1.3
1.5
2.4
4.0**
0.007
Diastolic RV/LV Area Radtio
1
0.8
1.6
2.4**, ***
0.0005
*: Significant difference between grade I and III (p<0.05). **: Significant difference between grade I and IV (p<0.05). ***: Significant difference between grade III and IV (p<0.05).
Ventilation MR Imaging 1. Hyperpolarized Noble Gas MR Imaging (HP-MRI)
• Qualitative and quantitative assessment of gas movement in airway and alveoli
• Difficulty for clinical installation due to gas and equipment issues
• Available in the limited western countries, and limited improvements of problematic factor for clinical set in near future.
2. Oxygen-Enhanced MR Imaging (O2-Enhanced MRI) • Qualitative and quantitative assessment of oxygen ventilation,
diffusion and uptake due to respiration • Difficulty for clinical installation due to MR sequence and
software issues, but no difficulty about equipment issue • Available in all over the world, and easier improvement of
problematic factors for clinical set
Hyperpolarized Noble Gas MRI (HP-MRI)
1. 3He: • Low solubility • High SNR due to high gyromagnetic ratio • High general polarization level (≥40%). • Cost: ≥ US$100/L • More limited availability in the clinical respiratory medicine
than 129Xe 2. 129Xe:
• Naturally abundant • Low SNR due to low gyromagnetic ratio • Low general polarization level (≤20%) • Cost:
Natural Xe gas (26%): ≤ US$20/L Higher concentration Xe gas (80%): ≥ US$700/L
• Anesthesia side effect • Higher clinical availability than 3He, if some problematic
factors solved
Oxygen-Enhanced MRI
O2-Enhanced MRI in Normal Subject O2-Enhanced MRI in COPD Subject
• Functional Assessment based on not only Ventilation, but also Oxygen
Diffusion in Smokers, COPD, Asthma and Lung Cancer.
• Potential for Directly Assessment of Respiration with High Spatial
Resolution and without Ionizing Radiation.
• No need for Special Equipment and Gas
Oxygen-Enhanced MRI vs. Quantitative CT -Smoking-Related COPD-
Cases
Age (years)
Smokers without COPD
40
62 ± 13
Mild COPD
40
62 ± 12
Moderate COPD
40
62 ± 11
Severe or Very Severe COPD
40
62 ± 10
Lifetime smoking exposure (pack years) 32 ± 27 35±23.7 62±22a, b 79 ± 43a, b
FEV1/FVC% (%)
%FEV1 (%)
85.4 ± 5.0
92.0 ± 10.2
65.0 ± 3.9a
84.8 ± 4.9a
51.1 ± 9.6a, b
62.4 ± 8.7a, b
37.3 ± 12.0a, b, c
37.7 ± 7.1a, b, c
%DLCO/VA (%) 90.4 ± 18.3 78.5 ± 13.8a 65.0 ± 15.0a, b 47.8 ± 13.6a, b, c
O2-enhanced MRI parameter 0.21 ± 0.07 0.16 ± 0.04a 0.13 ± 0.04a, b 0.09 ± 0.04a, b, c
Quantitative CT parameter 0.70 ± 0.14 0.61 ± 0.13 0.50 ± 0.16a, b 0.43 ± 0.18a, b
a: Significant difference with ‘Smokers without COPD’ group (p<0.05). b: Significant difference with ‘Mild COPD’ group (p<0.05). c: Significant difference with ‘Moderate COPD’ group (p<0.05).
Ohno Y, et al. Am J Respir Crit Care Med. 2008; 177: 1095-1102.
42-year-old male candidate for lung volume reduction surgery
Quantitative Thin-Section CT Perfusion SPECT/CT
O2-enhanced MRI before LVRS
O2-enhanced MRI after LVRS Ohno Y, et al. AJR Am J Roentgenol. 2012; 199: 794-802.
Correlations between Improvements in Oxygen-Enhancement
before and after LVRS and in Clinical Outcomes.
r=0.71, p<0.0001
r=0.64, p<0.0001
r=0.45, p=0.02
r=0.44, P=0.02
Ohno Y, et al. AJR Am J Roentgenol. 2012; 199: 794-802.
76-year-old smoking subject with Brinkman’s index of 1480
Mean wash-in time = 53.0 sec Mean relative enhancement ratio = 0.08
Ohno Y, et al. AJR Am J Roentgenol. 2008; 190: W93-W99.
Dynamic O2-Enhanced MR Parameters vs.
Pulmonary Function Test Results
r=0.80, r2=0.64,
p<0.0001
r=0.72, r2=0.52,
p<0.0001
r=0.72, r2=0.52,
p<0.0001
r=0.69, r2=0.48,
p<0.0001
r=0.78, r2=0.61,
p<0.0001
r=0.62, r2=0.38,
p<0.0001
Ohno Y, et al. AJR Am J Roentgenol. 2008; 190: W93-W99.
Quantitatively Assessed Ventilation Imaging by HP-MRI and O2-Enhanced MRI
• Ventilation imaging consisted with HP-MRI and O2-enhanced MRI is currently applied for academic purposes rather than clinical purposes due to some problematic factors that have to be solved for clinical set.
• O2-enhanced MR imaging has the potential to assess physiopathology and disease severity in COPD, asthma and interstitial lung disease due to connective tissue disease patients.
• O2-enhanced MR imaging can be play as one of the biomarkers for treatment and management as well as one of the predictors for patient outcome in pulmonary diseases.
• Quantitative software as well as sequence improvements and technical reproducibility will encourage O2-enhanced MR imaging in routine clinical practice.
Ohno Y, et al. Am J Respir Crit Care Med. 2008; 177: 1095-1102 Ohno Y, et al. AJR Am J Roentgenol. 2008; 190: W93-W99. Ohno Y, et al. Eur J Radiol. 2011 Jan;77(1):85-91. Ohno Y, et al. AJR Am J Roentgenol. 2012; 199: 794-802. Ohno Y, et al. Eur J Radiol. 2013 Nov 23 [Epub ahead of print]
Lung MRI with Ultra-Short TEs in Smokers on 3T System
Smokers wth Mild COPD Smokers wth Severe or very Severe COPD
CT-based FLV was 0.72, and WA% was 60 %
Mean T2* value for this subject was 0.75 msec.
CT-based FLV was 0.63, and WA% was 65 %
Mean T2* value for this subject was 0.67 msec.
Ohno Y, et al. AJR Am J Roentgenol. 2011; 197: W279-285.
Comparison of Capability for Difference AssessmentAmong All Clinical Stage in Smokers
-Quantitative CT vs. UTE-MRI-
Smokers
without COPD
Smokers with
mild COPD
Smokers with
moderate COPD
Smokers with severe or
very severe COPD
(Mean ± SD) (Mean ± SD) (Mean ± SD) (Mean ± SD)
CT-based FLV (%) 76.3 ± 5.8 73.6 ± 6.7 60.1 ± 8.5 a, b 51.8 ± 10.0 a, b
WA% (%) 63.1 ± 4.0 64.3 ± 4.9 66.6 ± 4.8 75.5 ± 2.9 a, b, c
Mean T2* value (ms) 0.82 ± 0.09 0.74 ± 0.07 0.61 ± 0.07 a, b 0.46 ± 0.11 a, b, c
SD: Standard deviation; CT-based FLV: CT-based functional lung volume; WA%: Ratio of WA to total airway area; T2*: T2 Star; a: Significant difference with smokers without COPD (p<0.05). b: Significant difference with smokers with mild COPD (p<0.05). c: Significant difference with smokers with moderate COPD (p<0.05).
Ohno Y, et al. AJR Am J Roentgenol. 2011; 197: W279-285.
Lung MRI with Ultra-Short TEs in CTD Patients on 3T System
53-year-old male assessed as normal subject
46-year-old female patient with mild interstitial lung disease due to PSS
65-year-old female patient with mild interstitial lung disease due to MCTD
Ohno Y, et al. Eur J Radiol. 2013; 82: 1359-1365.
Correlations of mean T2* value and CT-based disease severity with pulmonary functional test results and serum KL-6 for CTD patients only
-Quantitative CT vs. UTE-MRI-
Mean T2* Value CT-Based Disease Severity
r p value r p value
%FEV1
-0.06
0.86
-0.15
0.56
%VC
-0.53
0.02
-0.26
0.31
%DLCO
-0.63
0.0049
-0.53
0.02
Serum KL-6
0.52
0.03
0.44
0.07
Ohno Y, et al. Eur J Radiol. 2013; 82: 1359-1365.
Quantitative Morphological Change
Assessment by MRI with UTEs (UTE-MRI)
• Regional T2* measurement on MRI with UTEs (UTE-MRI) has a potential to quantitative assessment of morphological changes within the lungs due to smoking-related COPD and interstitial lung disease due to connective tissue disease, and may play as complementary role for management of these patients with quantitatively and qualitatively assessed thin-section CT.
• UTE-MRI is one of the new techniques for pulmonary functional MR imaging, and 3 Tesla system may have an advantages in this issue, although further investigations are warranted.
• Quantitative software as well as sequence improvements and technical reproducibility will encourage this technique in routine clinical practice.
Takahashi M, et al. J Magn Reson Imaging. 2010 ; 32: 326-33. Ohno Y, et al. AJR Am J Roentgenol. 2011; 197: W279-285. Ohno Y, et al. Eur J Radiol. 2013; 82: 1359-1365. Ohno Y, et al. J Magn Reson Imaging. 2013 Sep 30 [Epub ahead of print]
Conclusion
1. Quantitative methods in lung MRI can be applied not only
physiologic or physiopathologic assessments, but also evaluating
various pulmonary diseases.
2. Several techniques are ready as clinical applications, and others
will be clinically set in near future.
3. These techniques will be shifting from 1.5T to 3.0T MR systems as
clinical tools with developing new techniques and software in
near future.
4. Softwares for quantitative analyses as well as MR system,
sequence and reconstruction algorithm developments are key
issues for quantitative assessment for lung MRI.
Establishing a “Normal” Baseline in QCT
Eric A. Hoffman, Ph.D. Dept. of Radiology, University of Iowa Carver College of Medicine
Advanced Pulmonary Physiomic Imaging Laboratory - APPIL
Disclosure: EAH is a founder and shareholder in VIDA Diagnostics
Hoffman EA. JAP 59:468-480, 1985
Standardized Volumetric Lung Imaging Includes Coaching to a Standardized Lung Volume
Full
Lung
Sca
n
Density Threshold for Emphysema: -950/910 HU
Calculate % of voxels below -950 or -910 HU in each lung region
Air Trapping Density Threshold: <-856HU
Air Trapping vs. dAge
p-value < 0.0001
Air Trapping vs. Location
p-value < 0.0001
Air Trapping vs. Location (by dAge)
p-value < 0.0001
Air Trapping vs. Gender (by dAge)
p-value < 0.0001
6 Standardized Airway Paths
RB1 RB4 RB10
LB1 LB4 LB10
Female vs. Male Airway Wall Thickness 56M 76F Normal Non-Smokers
Branching Variations
Total (n=222)
Male (n=103)
Female (n=119)
Right upper trifurcation (B1, B2, B3) 51.8% 59.2% 45.4% bifurcation (B1+B2, B3) 15.8% 15.5% 16.0% bifurcation (B1+B3, B2) 14.9% 12.6% 16.8% bifurcation (B1, B2+B3) 17.6% 12.6% 21.8%
Right lower bifurcation (B8, B9+B10) 89.6% 91.3% 88.2% bifurcation (B8+B9, B10) 6.3% 3.9% 8.4% trifurcation (B8, B9, B10) 4.1% 4.9% 3.4%
Left upper bifurcation (superior divison, lingular) - bifurcation (B1+2, B3) 87.8% 86.4% 89.1% bifurcation (superior divison, lingular) - trifurcation 8.6% 8.7% 8.4% trifurcation (B1+2, B3, lingular) 3.6% 4.9% 2.5%
Left lower bifurcation (B8, B9+B10) 69.8% 66.0% 73.1% bifurcation (B8+B9, B10) 12.6% 13.6% 11.8% trifurcation (B8, B9, B10) 17.6% 20.4% 15.1%
From: Hoffman, Barr et al CT-Based Quantitative Lung Measures in a Healthy Multiethnic Sample: The MESA Lung Study (in review)
% Emphysema in Normals – Linear Model: From MESA Lung
From: Hoffman, Barr et al CT-Based Quantitative Lung Measures in a Healthy Multiethnic Sample: The MESA Lung Study (in review)
% Emphysema in Normals – Linear Model: From MESA Lung
% Emphysema in Normals – Linear Model: From MESA Lung
From: Hoffman, Barr et al CT-Based Quantitative Lung Measures in a Healthy Multiethnic Sample: The MESA Lung Study (in review)
Translating XeCT to Human Imaging
Non-Smoker Smoker
Xe-CT Differentiates between Normal Smokers and Normal Non-Smokers
Percentage of lung that does not curve fit
Non-Smoker (N=10) 23.78% w/o Fit
Smoker (N=3) 40.71% w/o Fit
Time Constants (sec)
M Fuld, EA Hoffman et al. ATS. 2009 San Diego, Abst 3530
Assessment of Regional Heterogeneity of Parenchymal Perfusion
J Appl Physiol. 2006 Nov;101(5):1451-65
Saline Lavage: HPB-Based Blood Flow Redistribution
EA Hoffman et al. Proc Am Thorac Soc. 2006 Aug;3(6):519-32
LPS Injury: Inhibition of HPB-Based Blood Flow Redistribution
EA Hoffman et al. Proc Am Thorac Soc. 2006 Aug;3(6):519-32
Normal vs. Pathologic Heterogeneity of Pulmonary Blood Flow
Proc Natl Acad Sci U S A. 2010 Apr 20;107(16):7485-90
NS SNI SCE
Mean Transit Time Deviations from Normal as a Phenotype of Emphysema Susceptibility
Proc Natl Acad Sci U S A. 2010 Apr 20;107(16):7485-90
Dual-Energy CT: Perfused Blood Volume (PBV) Material Decomposition to Identify Iodine Concentration
Fuld MK et al Radiology2013 Jun;267(3):747-56.
Dual Energy Imaging of Perfused Blood Volume
Perfused Blood Volume Pre vs. Post Sildenafil Response
Normal vs. Phenotype of Empysema Susceptible
NS SS
In collaboration with Krishna Iyer, John Newell Jr., and Sif Hansdottir
Regional Coefficients of Variation Pre and Post Sildenafil Normal vs. CE Susceptible Smoker
In collaboration with Krishna Iyer, John Newell Jr., and Sif Hansdottir
Total Pulmonary Vascular Volume
Assessment of Regional Lung Mechanics 1) Matching Small Scale Structures 2) Measuring Small Scale Changes
• Two lung CT scans are done at two different times at full inspiration (TLC).
• Local points are optimally matched by image registration to assess changes in small scale lung biomechanic signal.
Time 0 (flexible)
Time 1 (stationary)
+
+
Image Registration
Small Scale Changes
∆v +
–
∆v
Matched Small Scale (Acinar) Structures
In collaboration with Jiwoong Choi, John Newell Jr., and Ching-long Lin, M Milhem, T Knutson, J Tessier, M Kansy
Time 0 (flexible)
Time 1 (stationary)
+
+
Image Registration
Normal Control (Non-smoker) Very little change over time in the small scale biomechanic signal
Time 0 (flexible)
Time 1 (stationary)
+
+
Positive Control (Asymptomatic Smoker) Very little change over time in the small scale biomechanic signal
∆* ≥ 50%
Image Registration ∆* ≥ 50%
100%
20%
100%
20%
In collaboration with Jiwoong Choi, John Newell Jr., and Ching-long Lin, M Milhem, T Knutson, J Tessier, M Kansy
Cancer Subject (Sarcoma) 1) Initial Scan No Lung Metastases
2) Followup Scan with Lung Metastases 3) Small Scale Change in Biomechanic Effect Noted In Several Areas
Including the Growing Lung Nodules Between the Two Scans
Pre (flexible)
Met (stationary)
+
+
∆* ≥ 50% Image Registration
100%
20%
In collaboration with Jiwoong Choi, John Newell Jr., and Ching-long Lin, M Milhem, T Knutson, J Tessier, M Kansy
Cluster Analysis
CT Phenotypes Must Provide Insights into the Etiology of the Disease, Not Just Road
Maps of Destruction And….
In order to determine abnormal, one must first
establish normal!
Scientific Oral Presentations
1 Size and Collapsibility of Emphysema Holes on CT of COPD: Evaluation with a New Method SEO JB, Oh SY, Lee M, Kim N, Lee SM Purpose: Although the extent of LAA at CT of COPD is known to correlate with loss of lung function, the size and collapsibility of emphysema holes were rarely investigated. The purpose of this study is to assess the size and collapsibility of emphysema clusters with a new method and to correlate it with pulmonary functional test (PFT). Methods and Materials: Volumetric, inspiration and expiratory CT scans in 60 COPD patients were selected. After extracting the area of LAA using the threshold of -950 HU, a new method of size classification of LAA, which is repeated application of multiple low pass filter with various size of Gaussian kernel, was used. Emphysema clusters were divided into four groups; E1<1.5mm, 1.5mm≤E2<7mm, 7mm≤E3<15mm, 15mm≤E4. The difference in distribution of each group between inspiration and expiration CT was assessed. The results were compared with PFT with Pearson correlation. Results: The mean extents of LAA, E1, E2, E3, and E4 were 27.2%, 3.5%, 12.9%, 6.8%, 3.9% on inspiration CT, 16.5%, 1.66%, 7.8%, 4.2%, 2.9% on expiratory CT, respectively. Among the four clusters, E3 showed larger decline on expiratory CT than other clusters. Both E2 and E3 were significantly correlated with FEV1(r=-0.449 and -0.423, respectively). Conclusion: Emphysema clusters of sublobular size were most collapsible. Emphysema of acinar and sublobular size contributes most to the FEV1 decline. This study shows that in addition to the extent of LAA, the size of emphysema clusters at CT of COPD should be also considered in relation to the loss of lung function.
2 Automated Nodule Segmentation With Sub-voxel Accuracy Using Mutual Interaction of Pulmonary Segmentation Structures OGUZ I, Raffy P, Wood S Objectives: Automated segmentation of nodules from CT scans is challenging due to border ambiguity with neighboring structures such as vessels and the pleura, as well as poor boundary contrast of more subtle nodules. We developed a novel method that robustly segments nodules by allowing mutual interaction between the nodule and surrounding structures, leading to a highly accurate segmentation. Methods/Materials: Our method relies on a graph-based segmentation algorithm for mutually interacting objects and provides sub-voxel accuracy by operating on the 3-D space rather than the image grid. It is robust to image noise and local minima. Our method was tested on solid nodules in the public Lung Imaging Database Consortium (LIDC) database and evaluated against the manual segmentation of 4 experts. The slice thickness ranges from 0.45 to 5mm (average voxel size: 0.70 x 0.70 x 1.69mm), with thin-slice scans defined as slice thickness ≤ 1.25mm. The tube current range is 40-582 mA and tube voltage range 120-40 kVp. Results: Average surface distance between the automated and manual segmentation was 0.71 ± 0.51mm (SD) (1,002 subjects, n = 1,307 nodules), with sub-voxel accuracy in 96% of cases. Performance was not significantly different between thin- and thick-slice scans (p>0.5): 0.72 ± 0.55mm (thick; n=494) vs. 0.70 ± 0.46mm (thin; n=508). Average surface distance was 0.65 ± 0.39mm for isolated nodules (n = 534), 0.80 ± 0.64mm for juxtapleural nodules (n = 446) and 0.69 ± 0.48mm for juxtavascular nodules (n = 327). Conclusions: Our novel nodule segmentation algorithm can robustly segment nodules with sub-voxel accuracy. Mutually interacting segmentation of the nodule and neighboring structures yields more accurate results and helps avoid “leaks” into the pleura or vessels.
Scientific Oral Presentations (Cont.)
3 Computer-aided Classification of Diffuse Lung Disease Opacities by Use of Sparse Representation-based Method on 3D-CT Images KIDO S, Zhao W, Xu R, Hirano Y Objectives: To improve a computer-aided diagnosis method for classification of diffuse lung disease (DLD) opacities such as consolidation, ground-glass opacity, honeycombing, emphysema, diffuse nodules and normal on 3D-CT images by use of sparse representation (SR) based-methods. Method/Materials: K-SVD is an algorithm of deciding dictionary of SR in a singular value decomposition approach, and orthogonal matching pursuit (OMP) is the canonical greedy algorithm for sparse approximation. A combination of K-SVD and OMP is used for typical SR-based method. It can achieve satisfied recognition rates. However, it needs a lot of calculation time (Tcal). So, we replaced the K-SVD by K-Means to train the dictionary, and simplified OMP by selecting the desired number of atoms at one time (OMP1). We proposed three SR-based methods: SR1 (K-SVD+OMP), SR2 (K-Means+OMP), SR3 (K-Means+OMP1). We compared proposed methods with published artificial neural network classifier based method (AN) and published nearest neighbor classifier based method (NN). 1161 volumes of interest were (VOIs) adopted as training data to optimize the parameters of methods and train each method, and 1046 VOIs were used as test data for evaluation. Results: The SR-based methods achieved satisfied results (SR1:96.1%, SR2:95.6%, SR3:96.4%), compared with published methods (AN:75.8%, NN:65.1%). Furthermore, when K-SVD was replaced by K-Means, Tcal of dictionary learning was reduced (SR1:13520s vs. SR2:241s, SR3:350s). And, when OMP1 was used as a substitute of OMP, Tcal of recognizing one VOI was also reduced (SR1:1.27s SR2:0.29s vs. SR3:0.13s). Conclusions: The SR-based methods can achieve high recognition rates for classification of DLD opacities, and Tcal of SRs can be reduced by adopting K-Means and OMP1.
4 Dose Modulation in an Anthropomorphic Chest Phantom and its Relative Effects when using Single and Dual Energy Scan Modes NEWELL JD, Sieren JP, Guo J, Levy J, Hoffman EA Objectives: Quantitative CT for the assessment of lung structure and function depends on accurate measures of lung CT densities. We have evaluated 4D dose modulation on a dual energy scanner in both the single (SE) and dual energy (DE) scan modes. Methods: We used an anthropomorphic phantom (Phantom Labs, Salem, NY) that included lung equivalent material and an air filled trachea region. A Siemens Definition FLASH was used in both SE and DE scan modes with and without CareDose. Analysis was via placement of 3D cylindrical ROI’s down the center of the trachea and within each lung. Statistical analysis included basic statistics, Welch’s t-test. Results: There was a significant difference in tracheal air mean attenuation (p < 0.0001) as well as in the right and left lung (RL and LL) material (p<0.0001) using SE compared to DE with/without CareDose. There were significant differences in right (p<0.05) and LL (p<0.01) material attenuation using SE compared to SE+CareDose. There were no (p>0.05) SE vs. SE+CareDose differences in tracheal air values. There is no significant difference in RL and LL material (p>0.05) DE compared to DE+CareDose. There were significant tracheal air (p <0.05) DE vs. DE+CareDose differences. Conclusion: We have previously demonstrated that in the DE mode, the scatter correction used in DE mode serves to bring mean tracheal air closer to the expected -1000HU value. In this study, use of SE+CareDose significantly altered attenuation values of the RL/LL material compared to SE. DE+CareDose significantly changed the attenuation values of tracheal air compared to DE. These data suggest that dose modulation must be used consistently throughout a study since it does change lung density values even in SE mode where scatter correction has been employed.
Scientific Oral Presentations (Cont.)
5 Lung Lesion Volume Measurements in Simulated Reduced-dose CT Scans YOUNG S, Kim G, McNitt-Gray M Objective: To illustrate how lung lesion volume measurements may be affected when using reduced-dose CT scans. Methods: For this IRB approved study, CT scan data from 37 patients were obtained and anonymized. We simulated reduced-dose scans by adding noise to the clinical raw CT data, obtained prior to reconstruction of the clinical CT scan. The simulation approach has two benefits: (1) it allows us to “scan” the same patient at any arbitrary dose level without any additional exposure, and (2) it produces reduced-dose scans with the identical nodule position as in the original scan. For each clinical scan, reduced-dose scans were generated at dose levels ranging from 25% down to 3% of our current clinical dose protocols. On the reconstructed image data sets, we used a semi-automated, in-house CAD program to contour the lung lesions at each dose level. To evaluate the resulting lesion volumes, we calculated agreement between clinical and reduced-dose volumes using a Bland-Altman-style approach to determine mean percent volume difference and 95% confidence intervals (CI ) on the differences. Results: Initial results showed 95% CI ranging from 30% at the 25% dose level to 38% at the 3% dose level. Increased variability at 3% dose may result from increased noise under ultra-low dose conditions. Conclusion: Lung lesion volume measurements appear to be fairly consistent down to extremely low dose levels. However, measuring lesion volume is only one part of the radiologist’s task. Our reduced-dose simulation software provides a foundation for further analyses of the impacts of dose level on reader performance. In the future, we will evaluate reader repeatability, which will help to more clearly separate the effects of reduced dose scans from reader variability.
6 Determination of the Optimal Time-Window to Perform Wide Volume Dual Energy CT in a Swine Model of Pulmonary Embolism (PE) JIMENEZ-JUAN L, Dey C, Moghe S, Mehrez H, Homampour S, Paul NS Objective: To determine the optimal time-window after contrast injection to generate Dual Energy (DE) Iodine Maps. Methods: A swine model of PE was used (3 subjects, 40 kg). Autologous thrombotic material was injected via an 11F right femoral sheath and the locations of the emboli were confirmed with CT pulmonary angiography. Using wide volume (320x0.5mm) CT (Aquilion ONE, TMS, Japan), 10 sequential DECT scans were acquired every 4s, after injecting 40mL of non-ionic intravenous contrast (Visipaque 320mg/mL, GE Healthcare) at 4 mL/s. Iodine maps were created using a commercially available software (Body Perfusion, TMS, Japan). 2 observers independently compared the iodine maps for all DE acquisitions to determine which DE pairs resulted in similar perfusion information. Qualitative assessment was performed using a 3 point score (1=excellent, 2=moderate, 3=poor match). The observers were blinded to the anatomical locations of the PE demonstrated on the CTPA study. Quantitative analysis of the iodine maps was assessed by cross-correlation in time of entropy measures of the maps. Results: Qualitatively, iodine maps at 13s and 17s demonstrated the best perfusion data. The mean visual score for the three subjects was 1.77. Quantitative analysis in two subjects demonstrated a strong correlation between the iodine maps at 13s and 17s, more so than iodine maps at 9s and 21s (p<0.05). In the third subject the optimal iodine maps were obtained at 17s. Conclusion: Using a porcine PE model and 40mL of CM injection at 4 mL/s, the optimal time window for DECT is ≤ 4s and occurs 13-17s post injection of contrast.
Quantitative CT Imaging of the Lung Scientific Posters
Saturday, March 15, 2014
Image Quality Comparison of Low KV Imaging in CT Pulmonary Angiography Bijan B, Belashabadi S, Davoodi M, Kabir A Aim: Comparing quality of images utilizing 80 KV versus 100 KV. Methods: In a non-randomized triple blind parallel quasi-experimental study, we evaluated 140 patients with suspicion for pulmonary embolism by 80 or 100 KV CT angiography. Image quality was judged by discerning opacified segmental and subsegmental pulmonary arteries. Results: Quality of images was better using 80 KV (OR, 2.08). Vascular enhancement was significantly higher in all main, segmental and subsegmental arteries (P< 0.001) in 80 KV group. Mean number of measurable segmental arteries was also significantly higher among 80 KV group. Conclusion: Despite increased image noise, visualization of segmental & sub-segmental pulmonary arterial branches was improved in cases with 80 KV in comparison to 100 KV.
Dynamic Lung Perfusion with Wide Volume CT: Effect of Arterial Input on Arterial Flow Maps Jimenez-Juan L, Dey C, Homampour S, Mehrez H, Paul N Objective: To determine whether the location of the arterial input within the pulmonary trunk, left or right pulmonary artery influences the accuracy of dynamic CT lung perfusion maps. Methods: 54 dynamic wide volume CT perfusion scans were performed in 3 pigs using 30s continuous data acquisitions, 100kV, 50mAs, and 320x0.5mm detector providing 160mm coverage. Each subject had 9 pre-pulmonary embolus (PE) and 9 post-PE dynamic perfusion scans in combination of 3 flow rates (4, 6, and 8mL/s) and 3 contrast volumes (0.4, 0.6, and 0.8mL/kg). Perfusion analyses were carried out using maximum slope. Tissue region of interest (ROI) was prescribed in the lung parenchyma, and arterial input ROIs were placed in the pulmonary trunk (PT), left main (LM), and right main (RM) pulmonary arteries. For each arterial input, Time Density Curves (TDCs) were determined and perfusion maps were generated. Qualitative assessment was performed by two thoracic radiologists (R1, R2). The LM and RM perfusion maps were compared to the PT, using a 3 point scale (1= mismatch, 2=matching perfusion defects but visual difference in color maps; 3=identical). Pearson’s rank correlatio n coefficient (r) was used for statistical comparison of the perfusion maps and TDC. Results: Quantitative analysis demonstrated a strong correlation between the TDCs (r>0.83, p<0.005) and the perfusion maps (r> 0.99, p<0.005) for all injection rates/volumes, regardless the arterial input location. Qualitative analysis ascertained good agreement between the perfusion maps and the visual scores; R1= 2.8 ± 0.4 (range 2-3), R2= 2.6± 0.5 (range 2-3). Conclusion: Accurate lung arterial flow maps can be generated by selecting the pulmonary trunk, the left or the right pulmonary artery as arterial input locations.
Scientific Posters (Cont.)
Dynamic Lung Perfusion with Wide Volume CT: Impact of Bolus Length on Arterial Flow Values Jimenez-Juan L, Dey C, Homampour S, Mehrez H, Paul NS Objective: To assess whether the bolus length impacts the evaluation of dynamic CT lung perfusion maps determined with single input maximum slope (SIMS) analysis in a pig model. Method: 54 dynamic wide volume CT perfusion scans were performed in 3 pigs (40kg) using 30s continuous data acquisitions, 100kV, 50mAs, and 320x0.5mm detector configuration. Each subject had 9 pre-pulmonary emboli (PE) and 9 post-PE dynamic perfusion scans using 3 contrast volumes (16, 24, and 32mL) and 3 flow rates (4, 6, and 8mL/s). Perfusion analyses were performed using the SIMS approach and arterial flow (AF) perfusion maps were generated. For each contrast volume, the perfusion maps generated at 4 and 6 mL/s were compared to those acquired at 8 mL/s. Mean AF values and perfusion defects (PD) were determined. Spearman rank correlation of mean AF was calculated. Sensitivity and specificity to detect PD were also calculated. Results: Mean±SD AF values (mn-1) for 4, 6, and 8mL/s were 2.35±0.65, 2.73±0.76 and 2.91±0.65, respectively. There was strong correlation of mean AF values using 32mL of contrast volume at 6 and 8 mL/s, r=0.89, p=0.03. The sensitivity to detect PD was 0.78± 0.07 (0.67-0.84) using a contrast volume of 32mL at 6 mL/s and 0.64±0.14 (0.42-0.82) using a contrast volume of 32mL at 8 mL/s. The specificity to detect PD using a contrast volume of 32 mL at both 6 mL/s and 8 mL/s was>0.82. However, the sensitivity to detect PD using contrast volumes of 16 and 24 mL at any flow rate was <0.64. Conclusion: Optimal AF perfusion maps require an injection rate ≥6ml/s.
Validation of Simulated Tomosynthesis: Can it Replace Real Imaging for First Line Clinical Trials? Kicska G Objective: Digital tomosynthesis (DTS) is a relatively new imaging modality with multiple potential, yet to be proven, clinical applications. Unfortunately, clinical studies that test these applications are time-consuming, expensive and involve radiating volunteers. We report and validate a technique that produces simulated DTS (sim-DTS) images that may substitute for real DTS images in early clinical studies, thus accelerating identification of clinical applications. Materials and Methods: CT voxel phantoms were created using phantom and human CT data. These voxel phantoms were used to create simulate DTS images. The same phantoms and humans were imaged with real tomosynthesis. Real-DTS and sim-DTS images were then compared for out-of-plane blurring, signal to noise, modulation transfer function and image quality. Results: Out-of-plane blurring was near identical between sim and real DTS studies. Sim-DTS images have greater signal to noise compared to real-DTS (82.1 vs. 5.2). Modulation transfer function (MTF) is decreased for sim vs real-DTS images (36% vs 79% @ 4 lp/mm). Qualitatively, images from real and simulated studies are similar. Conclusions: Sim-DTS images produce near identical out-of-plane blurring compared to a real-DTS images, a major imaging feature of tomosynthesis. Sim-DTS images produced lower MTF response, a result of CT voxel phantom resolution. However, this is partly offset by higher signal to noise of sim-DTS images. These findings suggest that simulated exams can replace real-DTS images in certain first-line clinical investigations, thus identifying useful clinical applications faster, cheaper and without radiating patients. Applications deemed promising can be further investigated with a conventional clinical trial using only real-DTS.
Scientific Posters (Cont.)
Feasibility of Cardiac Output Measurements Based on CT Timing Bolus Imaging Using a Modified Timing Bolus Scalzetti EM, Rajebi H, Ogden KM Objectives: One way of selecting the optimal delay between contrast injection and CT pulmonary angiography (CTPA) is to obtain timing bolus images. The timing bolus method has been shown to allow estimation of cardiac output (CO), based on attenuation measurements in the aorta or main pulmonary artery (MPA) and application of the indicator dilution principle, fitting a gamma-variate curve to the time-attenuation data. We assessed the feasibility of calculating CO after we modified the volume of the timing bolus. Methods: Patients referred for evaluation of pulmonary embolism were identified retrospectively, with permission of the local IRB. In each case, 75 ml of a saline-contrast mixture were administered intravenously at a rate of 5 ml/s as a modified timing bolus, using a dual-headed power injector. Beginning at 4 s, 40 mm CT volumes were obtained every 2 s until contrast washed out of the MPA. Attenuation measurements were made in the MPA and aorta. This was followed by the CTPA, for which 75 ml of contrast were given at a rate of 5 ml/s. Separately, the relationship between CT attenuation and iodine concentration was assessed in a phantom. The time-attenuation data was modeled with a gamma-variate curve, and the CO calculated. Results: CT attenuation varied linearly with iodine concentration (slope 26 HU/mg/ml). The average r2 goodness of fit for the gamma-variate model was 0.90 (95% CI 0.74-0.97). The average CO was 4.3 L/min (95% CI 2.9-8.5), compared to a reported normal range of 4.0-8.0 L/min. Conclusions: Reasonable values for CO were obtained by applying an indicator-dilution approach to a modified timing bolus of contrast, the volume of which is equal to the contrast volume given for the CTPA. Continued work on validation seems to be warranted.
Reproducibility of Cardiac Output Measurements Based on CT Timing Bolus Imaging Using a Modified Timing Bolus Scalzetti EM, Rajebi H, Ogden KM Objectives: Cardiac output (CO) can be calculated from measurements made in the main pulmonary artery (MPA) on timing bolus images that are obtained as part of a CT pulmonary angiogram (CTPA). We implemented this indicator dilution-based method with a modified timing bolus. We identified patients who had two or more CTPAs but lacked acute abnormalities, and compared the calculated COs. Methods: Patients referred for pulmonary embolism evaluation were identified retrospectively, with permission of the local IRB. In each case, 75 ml of a saline-contrast mixture were administered intravenously at a rate of 5 ml/s as a modified timing bolus, using a dual-headed power injector. Beginning at 4 s, 40 mm CT volumes were obtained every 2 s until contrast washed out of the MPA. Attenuation measurements were made in the MPA and aorta. This was followed by CTPA, for which 75 ml of contrast were given at a rate of 5 ml/s. The attenuation of unenhanced blood in the aorta was subtracted from the MPA attenuation values. The time-attenuation data was modeled with a gamma-variate curve, and the CO calculated. Paired values of CO were compared (SigmaPlot). Results: 4 patients had 2 CTPAs and 1 patient had 3 CTPAs during the study period, yielding 6 CO pairs. All pairs but one differed by <10%. Averages of paired values did not correlate with differences within pairs (slope of regression line 0.005). The average r2 goodness of fit for the gamma-variate model was 0.85 (range 0.72-0.96). Conclusions: The method, as applied to the modified timing bolus, had good reproducibility in this small sample of patients who had repeat examinations made under similar clinical conditions. Differences do not appear to track with the magnitude of CO. Continued work on validation seems to be warranted.
Scientific Posters (Cont.)
Distensibility Differs Regionally in Asthma vs. Normal Controls: A CT Investigation in SARP subjects Shim SS, Schiebler ML, Sorkness RL, Jarjour NN, Kanne J, Fain SB Purpose: To assess the change of airway lumen area from the 1st-6th generations on inspiratory and expiratory computed tomography (CT) in asthmatics as compared with healthy normal subjects. Methods: We enrolled 185 SARP patients (152 asthmatics and 33 controls). There were corresponding to 3681 airway segments in asthmatics and 820 in normal subjects evaluated. The difference between lumen areas was calculated for the 1-6th generation of the airway tree in CT images using VIDA software, acquired at total lung capacity (LAt) and functional residual capacity (LAf). Distensibility was defined by following equation: 100 x [1-(LAt/LAf)]. Differences in distensibility in 1-6th generations of the airway tree was then compared for asthma vs. control groups. Results: Mean value of distensibility of all generations differed and was 20.6% in asthmatics and 16.9% in normal subjects (p<0.001). By generation, distensibility of asthmatics for 1st-6th generation respectively was: 15.7%, 16.2%, 20.4%, 22.8%, 20.3% and 18.7%, whereas for normal controls was: 13.3%, 16.7 %, 15.6%, 16.2% 17.8%, and 20.6 %. Differences at the 3rd (p=.048), 4th (p<.001), and 5th (p=.124) generations were significant or trending towards significance. Qualitatively, a steady increase of distensibility was observed from central to more distal airway generations for normal controls, whereas, distensibility in asthmatics this steady increasing trend with generation only held for the 1-4th generations decreasing thereafter in the more distal 5-6th generations. Conclusion: Asthmatics demonstrate a larger distensibility than normal subjects, particularly in 3rd and 4th airway generations and demonstrate a pattern of decreasing airway lumen change in the more distal airways when compared to normal subjects.
Airway Distensibility in Asthma Patients by Computed Tomography: Correlation with Clinical Indices and Reversibility after Bronchodilator Therapy Shim SS, Schiebler ML, Sorkness RL, Jarjour NN, Denlinger LC, Fain SB Purpose: The aim of this study is to assess airway distensibility found on computed tomography (CT) in asthma patients and to explore correlation with clinical indices and response to bronchodilator therapy. Methods: We prospectively enrolled a total of 152 Severe Asthma Research Protocol (SARP) subjects. The airway distensibility was defined as the ratio of difference between airway lumen area found at CT during total lung capacity (TLC) and functional residual capacity/ lumen area at TLC. Distensibility of all 1st-6th generation airways was measured using VIDA software, and the mean values for each subject were calculated. Correlations were made between mean value of distensibility and clinical indices including sex, age, body mass index (BMI), severity, and FEV1 pre- and post-bronchodilator therapy to determine reversibility. Results: Univariate analysis showed significant correlation with airway distensibility for the following clinical indices: BMI (r=0.271, p=0.001), pre-bronchodilator FEV1 %predicted (r=0.160, p=0.048), pre-bronchodilator FEV1/FVC (r=0.209, p=0.01), post-bronchodilator FEV/FVC (r=0.201, p=0.015) and max bronchodilator FEV1 reversibility (r= - 0.168, p=0.039). Using a multivariate regression analysis, average airway distensibility in asthmatics was dominated by BMI (p<0.001). BMI in females was also found to be highly correlated with distensibility, but this was not found for male subjects. Conclusion: In SARP patients, increased airway distensibility is positively associated elevated BMI in females and negatively associated with the % reversibility of bronchodilator therapy. In combination, these findings suggest increased distensibility may help distinguish different patterns of air flow obstruction in asthma.
Scientific Posters (Cont.)
Lobar Contribution to Lung Volume and Heterogeneity in Healthy Subjects: Quantitative Assessment by CT Silva M, Nemec S, Dufresne V, Bankier AA Objectives: To quantify the contributions of individual lobes to lung volume, and to characterize quantitative morphological metrics associated with these volumes. Methods/Materials: 17 healthy individuals underwent spirometrically monitored volumetric chest CT at total lung capacity (TLC), functional residual capacity (FRC), and mean inspiratory capacity (MIC), measured half-way between TLC and FRC. A semiautomatic software for parenchymal quantification (Pulmo3D, MeVis, Germany) was used to measure lobar volume, lobar density (MLoD and MLoD%) and the standard deviation of density, as a surrogate parameter for parenchymal heterogeneity. Differences in lobar metrics between individual lobes, and between lung volumes were assessed with analyses of variance for repeated measurements and with unpaired T tests. Results: Lobar contributions to TLC were: RUL 17%; RML 8%; LUL 21%; RLL 27%; LLL 27%. Lobar contributions to MIC were: RUL 18%; RML 9%; LUL 22%; RLL 26%; LLL 26%. Lobar contributions to FRC were: RUL 18%; RML 11%; LUL 25%; RLL 24%; LLL 23%. At TLC, the relative contribution of the lower lobes to lung volume was bigger than at FRC (p<0.001), while reverse phenomenon was observed for the upper lobes (p<0.001). The lower lobes were significantly denser than all other lobes, notably at FRC (ΔMLoD 76 ± 55 HU, p<0.001). Heterogeneity was higher in the lower lobes than in all other lobes, again, notably at FRC (p<0.001). Conclusions: The lower lobes make the biggest contribution to overall lung volume and, at any lung volume, are denser and more heterogeneous than other lobes. Our results confirm the feasibility of in-vivo assessment of lobar metrics and provide normative data of lobar volumes, density, and heterogeneity in healthy volunteers.
2014 Quantitative CT Imaging of the Lung Disclosures As a provider accredited by ACCME, the Society of Thoracic Radiology must ensure balance, independence, objectivity and scientific rigor in its educational activities. Course Director(s), Planning Committee Members, Faculty, and all others who are in a position to control the content of this educational activity are required to disclose all relevant financial relationships with any commercial interest related to the subject matter of the educational activity. Safeguards against commercial bias have been put in place. Faculty will also disclose any off-label and/or investigational use of pharmaceuticals or instruments discussed in their presentation. Disclosure of these relevant financial relationships will be published in course materials so those participants in the activity may formulate their own judgments regarding the presentation. Listed below are the Quantitative CT Imaging of the Lung Course Course Directors, Review Committee Members, Faculty and Scientific Oral Presenters and their co-authors:
1) who have disclosed and the potential conflict has been resolved.
Full Name Commercial Interest What was Received Role Sean B. Fain, PhD GE Healthcare Grant Support Principal
Investigator Xerned LLC Honorarium Scientific Advisory
Board GE Healthcare Research Grant Principal
Investigator Xemed LLC Honorarium Scientific Advisor
Jonathan Goldin, MBChB, PhD MedQia Ownership Board Member
Junfeng Guo, PhD Share holder in VIDA diagnostics, a
company commercializing lung image analysis software developed, in part, at the University of Iowa.
nothing received within the past 12 months
share holder
Eric A. Hoffman, PhD VIDA Diagnostics Stock My lab utilized data analysis software from VIDA, a company for which I am a founder and share holder
Jeffrey Kanne, MD Perceptive Informatics Consulting Fee Independent Consultant (not relevant to material at hand)
Namkug Kim, PhD None None Software developing and experimental study
Joshua Levy, BS Image Owl, Inc. The Phantom Laboratory is a stock holder
Manager
The Phantom Laboratory, Incorporated
I am an employee and stock holder
President and Responsible for Engineering and Research and Development
David A. Lynch, MB Perceptive Imaging, Boehringer Ingelheim, Genentech, Veracyte, Gilead, Intermune
Consulting Fee Consultant
Centocor, Siemens, NHLBI Research Support Researcher
Michael McNitt-Gray, PhD Consultant, Fulbright and Jaworski, LLC
Consultant Fees Consultant
Flaherty Sensabaugh Bonasso PLLC Consultant Fees Consultant Siemens Healthcare Institutional Research
Support
Investigator
Siemens Healthcare Grant Support Investigator Hatem Mehrez, PhD Toshiba Medical Systems, Canada Salary Clinical Research
Physicist
Sachin Moghe, PhD Toshiba Medical Research Institute, USA
Salary Research
John D. Newell, Jr., MD VIDA Diagnostics Money, Stock Options Consultant, Advisory Board
Siemens Health Research Grant Research Support Siemens HealthCare AG Research Money. Travel
Money Researcher
VIDA Diagnostics Inc. Money for Consulting. Stock Options.
Paid Consultant
Ipek Oguz, PhD VIDA Diagnostics Salary Employee
Yoshiharu Ohno, MD, PhD Toshiba Medical Sustems Corporation
Research Grant Principal Investigator
Philips Electronics Japan Research Grant Principal Investigator
Bayer Pharma Research Grant Principal Investigator
Guerbet Japan Research Grant Principal Investigator
Narinder Paul, MD Research Support Toshiba Medical Systems
Reserach grant PI
Philippe Raffy, PhD I am an employee of VIDA Diagnostics Inc. Co-author
Joyce D. Schroeder, MD Siemens Corp. Research Salary Support Principal Investigator on Siemens-funded CT Study
Joon Beom Seo, MD Guerbet Korea Honorarium Speaker Siemens Honorarium Speaker
Susan Wood, PhD VIDA Diagnostics Employee CEO
2) who declared no relevant financial relationship(s) with industry
Soudabeh Belashabadi, MD, MBA Bijan Bijan, MD, MBA Mohammad Davoodi, MD Pim de Jong, MD, PhD Loren Denlinger, MD, PhD Chris Dey, MD Valarie Dufresne, MD Raul San Jose Estepar, PhD Yasushi Hirano, PhD Shabnam Homampour, MASc Nizar Jarjour, MD Laura Jimenez-Juan, MD Philip F. Judy, PhD Ali Kabir, MD, PhD Shoji Kido, MD, PhD Grace Hyun Kim, PhD Sang Min Lee, MD Minho Lee, MS Francesco Molinari, MD Stefan Nemec, MD Kent Ogden, MD Sang Young Oh, MD Hamid Rajebi, MD Geoffrey D. Rubin, MD Ernest Scalzetti, MD Mark Schiebler, MD Sung Shine Shim, MD Jered Sieren, BS Mario Silva, MD Ronald Sorkness, PhD Daniel C. Sullivan, MD George R. Washko, MD Rui Xu, PhD Stefano Young, PhD Wei Zhao, MS