grid technology and medical imaging derek hill division of imaging sciences gkt school of medicine,...

Grid technology and medical imaging

Derek Hill

Division of Imaging Sciences

GKT School of Medicine, Guy’s Hospital

Derek.Hill@kcl.ac.uk

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Summary

• Some observations of how computing and imaging has developed in medical imaging

• What can the grid offer medicine and healthcare• Two demonstrators:

– Image-based decision support– Image analysis for drug discovery

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Applications of medical imaging

• Healthcare– Patient diagnosis

– Patient treatment

– Screening

– Quality assurance

• Medical research– Cohort comparisons

– Longitudinal studies

• Drug discovery– Surrogate end-points in drug trials (pre-clinical and clinical)

• Device development– Next generation orthopaedic implants

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Image guided interventions Images CourtesyGuy’s Hospital

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Image guided interventions II Images CourtesyGuy’s Hospital

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Labelling structures using a reference atlas

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Labelling patient images in database

Reference image(example slice)

Database subject image(example slice)

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Example labelled subject

Example database subject to whom labelled reference image has been warped

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Surgical verificationAccuracy of surgical placement against plan

• Surgeon plans on X-ray or CT, uses database of prostheses• Operation takes place using plan as guidance• Post operative X-ray evaluated for accuracy of placement• Data stored and used for short term assessment and long term evaluation

studies

Courtesy of Ian RevieDepuy International

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Support for Multidisciplinary Collaborative Environments:Triple Assessment of Breast Cancer Patients (MIAKTS)

• Surgeons• Radiologists• Pathologists• Oncologists• Nurses

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Multidisciplinary Management of Breast Cancer

Radiology

Pathology

Surgery

Images courtesy of Oxford and Guy’s

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Magnetic resonance imaging for breast screening (MARIBS)• Is MRI an effective way of screening young women

at high risk of breast cancer?• 17 Centres in the UK (and associated with other

large trials in Europe and Canada)• Led by the Institute of Cancer Research• MRC and NHS funded study

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Complex processing for MARIBS and to support triple assessment

Pre-contrast Post-contrast

MR Mammogram

Model deformation

Shape and texture analysis

Images courtesy of Guy’s Hospital and KCL

Subtracted projection

Non-rigid registration

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How e-science can help

• E-science is providing:– An easy-to-use registration service to align and process the

images– Image-derived metadata that can be queried for clinical

decision support or for research– Ontologies to improve interoperability of data sources.

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Bone Disease:

What changes do we see in Osteo Arthritis?

1. Joint Space narrowing

2. Changes in Texture

3. Changes in ‘banding’

Courtesy Chris Buckland-Wrightand Lewis Griffin

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Biologists explanations of these changes involve multiple scales.

Causation flows from fine to coarse

& back down again

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Model at multiple scales

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Distributed computing for mega-scale modelling.

+

+

+-

-

Fine Medium Large Fine Medium Large

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Size of medical images

• An individual 2D medical image is quite small– Nuclear medicine: 32kByte– Magnetic Resonance Imaging (MRI): 128kByte– X-ray Computed Tomography (CT): 512kByte– X-ray angiogram: 1Mbyte– Chest x-ray: 16Mbyte

• One patient study is quite large– Eg: 1 heart study in MRI is typically 1Gbyte

• Aggregated data from cohorts can be very large– Eg: analysis of 500 subjects

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Image metadata

• Details of image acquisition– Modality– Details of acquisition (modality specific)– Geometrical information – Timing information

• Information about patient– Name, address, doctor’s name, patient identifier– Past medical history, family history, social history– Presenting complaint, differential diagnosis

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Characteristics of medical image analysis software• Real-time interaction

– Viewing and manipulation of 3D volumes and 2D/3D+time data

– Interactive structure delineation

• Automatic algorithms– Rapid evolution of algorithms (not based on legacy code)– Major area of international research– Algorithm complexity increases faster than Moore’s law– Frequently generate substantial derived information

• Many times the size of the original data

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Medical image storage

• Historically, images have been printed onto film for storage, and archived in removable media that are usually unreadable after about 3 years

• Digital medical image archives are becoming standard (especially in Japan!)

• Patient image storage is distributed (patients often visit many hospitals over course of their life)

• Many research studies involve multi-site image acquisition

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Some Observations

• Medical and healthcare industry and hospitals do not regularly use complex information processing, – It is not part of their core business to invest in the implementation

and support of this activity– uptake has been disappointing

• Imaging research and development in academic labs often stops with the publication of a new method/algorithm– Yet over the last decade we have seen major advances in many

aspects of this technology (image interpretation, segmentation, shape analysis, registration, visualisation, ..)

• There is little data sharing except multicentre research studies where all images send on removable media to central analysis site.

• International data sharing is problematic

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• Computing is not a core business of healthcare organisations and related companies (eg: pharma)

• The market is used to paying for services as needed (eg: image acquisition is paid for on a per-patient basis, analysis could be the same)

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Potential benefits of the grid

• More effective sharing of data• More efficient multi-professional working in patient

management• Access to substantial on-demand computing

resource• New “collaborations of equals” in which multicentre

studies have full scientific input from all sites• New ways of image analysis needs being met

– Eg: new companies delivering grid services to healthcare and pharmaceutical industry.

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Two example applications

• Image-based decision support• Analysis of images for drug-discovery

A dynamic brain atlas

Grid-enabled decision support in healthcare

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Context

• Better information management is a high priority in the modernization of the NHS.

• Decision support is a key component– Existing example: prompting doctor with contra-indications

of selected medicines

• We show how the grid can bring image-based decision support– Calculating a customized brain atlas on the fly

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Workflow of busy radiologist

Load patient image from worklist

diagnosisEasy?YesNo

Usetext book

atlas

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Workflow of busy radiologist

Load patient image from worklist

diagnosisEasy?Yes

Usepatient specificDynamic atlas

Viewingtools

No

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…need reference data

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200 reference subjects

Example slicesFrom MRI Volumeimages

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Patient scan+ instructions

OxfordUniversity

King’s College London

(Guy’s Campus)

Get reference imagesIMPERIALIMPERIALCOLLEGECOLLEGE

KING’S COLLEGE KING’S COLLEGE LONDONLONDON

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OxfordUniversity

King’s College London

(Guy’s Campus)

IMPERIALIMPERIALCOLLEGECOLLEGE

KING’S COLLEGE KING’S COLLEGE LONDONLONDON

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OxfordUniversity

King’s College London

(Guy’s Campus)Create atlas

atlas

IMPERIALIMPERIALCOLLEGECOLLEGE

KING’S COLLEGE KING’S COLLEGE LONDONLONDON

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The Radiologist’s view

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Conclusions

• The dynamic atlas provides a customized authoritative reference presented in an intuitive way

• The doctor can see at a glance the normal range of sizes and shapes of each brain structure, overlaid on the patient’s own scan, assisting diagnosis.

• The grid will bring new ways of working to Healtcare

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Team

• Derek Hill, Thomas Harkens, Kate McLeish, Colin Renshaw, King’s College London (Guy’s Campus) Derek.Hill@kcl.ac.uk

• Jo Hajnal, Imperial College London (Hammersmith) jhajnal@ic.ac.uk

• Daniel Rueckert, Imperial College London (South Ken) dr@doc.ic.ac.uk

• Steve Smith, University of Oxford steve@fmrib.ox.ac.uk

Grid services in the drug-discovery workflow

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Context

• Pharmaceutical companies are major users of imaging

• They need validated automated image analysis to quantify drug efficacy for surrogate endpoints

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Drug discovery

The Grid

Bone labelling service,brain labelling service, …

Scientist

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Demonstrator system

IXIGSK

Image registration service

1. Locate image data2. Transfer data by ftp or grid-ftp*

3. Registration job running

4. Download results

*think of grid-ftp as a secure version of ftp

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Commercial opportunities

• Specialist companies will provide complex information processing services (eg in medical image analysis)

• They will purchase computing resource as needed• Their customers will be:

– Hospitals, PCTs– The pharmaceutical industry– Medical devices industry– Government agencies

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Conclusions

• Medical imaging is well suited to grid capabilities• There are particular problems of security and

confidentiality• There is less legacy s/w and hardware in medical

imaging than in some other scientific and engineering applications

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Thankyou