Grid technology and medical imaging

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Grid technology and medical imaging

• Derek Hill
• Division of Imaging Sciences
• GKT School of Medicine, Guys Hospital
• Derek.Hill_at_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 Courtesy Guys Hospital
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Image guided interventions II
Images Courtesy Guys 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 Revie Depuy International
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Support for Multidisciplinary Collaborative
EnvironmentsTriple Assessment of Breast Cancer
Patients (MIAKTS)

• Surgeons
• Radiologists
• Pathologists
• Oncologists
• Nurses

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Multidisciplinary Management of Breast Cancer
Pathology
Images courtesy of Oxford and Guys
Radiology
Surgery
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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
MR Mammogram
Images courtesy of Guys Hospital and KCL
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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 DiseaseWhat changes do we see in Osteo
Arthritis?
Courtesy Chris Buckland-Wright and Lewis Griffin
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Biologists explanations of these changes involve
multiple scales.
Causation flows from fine to coarse
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Model at multiple scales
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Distributed computing for mega-scale modelling.
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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, doctors 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/3Dtime 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
  Moores 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

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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
Easy?
Yes
No
diagnosis
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Workflow of busy radiologist
Load patient image from worklist
Easy?
Yes
No
diagnosis
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need reference data
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200 reference subjects
Example slices From MRI Volume images
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KINGS COLLEGE LONDON
IMPERIAL COLLEGE
Kings College London (Guys Campus)
Oxford University
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KINGS COLLEGE LONDON
IMPERIAL COLLEGE
Kings College London (Guys Campus)
Oxford University
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KINGS COLLEGE LONDON
IMPERIAL COLLEGE
Kings College London (Guys Campus)
Create atlas
Oxford University
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The Radiologists 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 patients 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, Kings College London (Guys Campus)
  Derek.Hill_at_kcl.ac.uk
• Jo Hajnal, Imperial College London (Hammersmith)
  jhajnal_at_ic.ac.uk
• Daniel Rueckert, Imperial College London (South
  Ken) dr_at_doc.ic.ac.uk
• Steve Smith, University of Oxford
  steve_at_fmrib.ox.ac.uk

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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
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Demonstrator system
3. Registration job running
2. Transfer data by ftp or grid-ftp
IXI
1. Locate image data
GSK
4. Download results
Image registration service
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