1. Grid technology and medical imaging Derek Hill Division of Imaging Sciences GKT School of Medicine, Guy’s Hospital Derek.Hill@kcl.ac.uk

2. N+N meeting 2003 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

3. N+N meeting 2003 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

4. N+N meeting 2003 Image guided interventions Images Courtesy Guy’s Hospital

5. N+N meeting 2003 Image guided interventions II Images Courtesy Guy’s Hospital

6. N+N meeting 2003 Labelling structures using a reference atlas

7. N+N meeting 2003 Labelling patient images in database Reference image (example slice) Database subject image (example slice)

8. N+N meeting 2003 Example labelled subject Example database subject to whom labelled reference image has been warped

9. N+N meeting 2003 Surgical verification Accuracy 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

10. N+N meeting 2003 Support for Multidisciplinary Collaborative Environments: Triple Assessment of Breast Cancer Patients (MIAKTS) Surgeons Radiologists Pathologists Oncologists Nurses

11. N+N meeting 2003 Multidisciplinary Management of Breast Cancer Radiology Pathology Surgery Images courtesy of Oxford and Guy’s

12. N+N meeting 2003 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

13. N+N meeting 2003 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

14. N+N meeting 2003 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.

15. N+N meeting 2003 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-Wright and Lewis Griffin

16. N+N meeting 2003 Biologists explanations of these changes involve multiple scales. Causation flows from fine to coarse & back down again

17. N+N meeting 2003 Model at multiple scales

18. N+N meeting 2003 Distributed computing for mega-scale modelling. + + + - - Fine Medium Large

19. N+N meeting 2003 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

20. N+N meeting 2003 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

21. N+N meeting 2003 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

22. N+N meeting 2003 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

23. N+N meeting 2003 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

24. N+N meeting 2003 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)

25. N+N meeting 2003 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.

26. N+N meeting 2003 Two example applications Image-based decision support Analysis of images for drug-discovery

27. A dynamic brain atlas Grid-enabled decision support in healthcare

28. N+N meeting 2003 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

29. N+N meeting 2003 Workflow of busy radiologist Load patient image from worklist diagnosis Easy? Yes No Use text book atlas

30. N+N meeting 2003 Workflow of busy radiologist Load patient image from worklist diagnosis Easy? Yes Use patient specific Dynamic atlas Viewing tools No

31. N+N meeting 2003 …need reference data

32. N+N meeting 2003 200 reference subjects Example slices From MRI Volume images

33. N+N meeting 2003 Patient scan + instructions Oxford University King’s College London (Guy’s Campus) Get reference imagesIMPERIALCOLLEGE KING’S COLLEGE LONDON

34. N+N meeting 2003 Oxford University King’s College London (Guy’s Campus) IMPERIALCOLLEGE KING’S COLLEGE LONDON

35. N+N meeting 2003 Oxford University King’s College London (Guy’s Campus) Create atlas atlas IMPERIALCOLLEGE KING’S COLLEGE LONDON

36. N+N meeting 2003 The Radiologist’s view

37. N+N meeting 2003

40. 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

41. N+N meeting 2003 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

42. Grid services in the drug-discovery workflow

43. N+N meeting 2003 Context Pharmaceutical companies are major users of imaging They need validated automated image analysis to quantify drug efficacy for surrogate endpoints

44. N+N meeting 2003 Drug discovery The Grid Bone labelling service, brain labelling service, … Scientist

45. N+N meeting 2003 Demonstrator system IXI GSK Image registration service 1. Locate image data 2. Transfer data by ftp or grid-ftp* 3. Registration job running 4. Download results *think of grid-ftp as a secure version of ftp

46. N+N meeting 2003 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

47. N+N meeting 2003 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

48. N+N meeting 2003 Thankyou