Image mapping, registration and atlases

1. Image mapping, registration and atlases Derek Hill Imaging Sciences School of Medicine King’s College London

2. Overview • Definitions • Applications • Medical Research • Diagnosis • Therapy planning and guidance • Drug discovery • E-science issues • Breakout group sub-headings

3. Definition of registration • Determining the transformation,or mappingT that relates positions in image A, to positions in a second image (or physical space) B. • When registered, a position x in image A and position T(x) in image B are the same position in the object.

4. Correspondence • Registration is a technique for aligning images so that corresponding features can be related. • For image-to-physical space registration, we determine correspondence between an image and physical positions identified with a 3D localiser

5. Atlases • Can mean several things • Single reference subject used to assist in analysis of others • Combination of multiple reference subjects • intensity average • Representation of variability • Pre-labelled dataset used for image segmentation • Registration required to: • Create atlas if it is from multiple subjects • Map atlas to patients or research subjects

6. Registration examples • Multimodality • Image guided surgery/therapy • Detecting change over time • Identifying differences between groups

7. PET-CT registration MRC cyclotron unit

8. MR-CT registration CT MR CT bone overlaid on MR After affine transformation

9. Multi-modal volume rendering (Ruff 1994) Hill et al Radiology 191:447-454 1994

10. Registration for image guided surgery

11. MRI tumour surface overlaid in microscope Edwards et al IEEE TMI 19:1082-1093 2000

12. 2D + 3D registration for therapy guidance • MRI + x-ray

13. Combining MRI and x-ray • Case 2. Electrophysiology study and RF ablation. • 3D multiphase SSFP MR sequence 3 phases, 256x256x128, 1.13x1.13x1.0mm3, TR=3.1ms, TE=1.6ms, =45 • Tracked biplane x-ray LAO AP

14. Registered MRI and catheters

15. Non-rigid registration • The previous examples have all assumed that the mapping has the degrees of freedom of a rigid body • Tissue deformation, image distortion and intersubject variability mean more degrees of freedom are needed to establish corresondence

16. Pre-contrast Post-contrast Subtraction Post-contrast (rigid registration) Post-contrast (affine registration) Post-contrast (non-rigid registration) Subtraction (rigid registration) Subtraction (affine registration) Subtraction (non-rigid registration)

17. MIP rendering Non-rigid registration Rigid registration No registration Rueckert et al IEEE TMI 18: 712-721 1999

18. Intersubject comparisons 8 subject average Rigid registration Affine registration Rueckert, from “Medical Image Registration” Hajnal, Hill, Hawkes (eds) CRC Press 2001

19. Intersubject comparison 8 subject average, non-rigid registration using 10mm grid Rueckert, from “Medical Image Registration” Hajnal, Hill, Hawkes (eds) CRC Press 2001

20. Using deformation fields in neuroscience research Nature, 9 March 2000

21. contracting expanding 6 month interval, baseline 2 years prior to symptoms Fox et al Lancet 358:201-205 2001

22. contracting expanding 29 month interval, symptoms appearing Fox et al Lancet 358:201-205 2001

23. contracting expanding 5 year interval, symptomatic for 2+ years Fox et al Lancet 358:201-205 2001

24. Intersubject comparison by Voxel Based Morphometry(provided by Colin Studolme, UCSF) PD MRI Tissue Segmentation (from PD+T2+T1) Regional Tissue Label Density Filter Regional Gray matter Density

25. Subj 1 Subj 1 Subj 2 Subj 2 Subj N Subj M Group Comparison of Local Gray Matter Density Age Matched Normal Group Test Group: FTD or AD Estimate Warp to Map Each Individual Anatomy to Common Coordinates Warp Tissue Density Maps to Common Coordinates Compare Tissue Density In Common Coordinates

26. E-science issues • Algorithms run slowly: excellent candidates for grid services • Aggregation of data needed to answer medical research and drug discovery questions • Variety of ancillary metadata formats • Rich and large intrinsic metadata. • Collaborative working desirable for healthcare and research • Curation currently poor

27. Possible Breakout group sub-headings • How do we make image registration grid services intraoperable? • Do we need to devise an abstract model for these services? • How should we represent mappings? • Do we need an ontology? • Should we use grid-services for a major cross validation of algorithms? • How can, or should,atlases be shared? • How could these services be used commercially (eg: for drug discovery)

29. Results with model

30. Method – Optical Tracking • Registration by optical tracking • X-ray table & c-arm are tracked by Optotrak • Sliding patient table is tracked by MR system

31. Method – Registration Matrix Calculation • Overall registration transform is composed of a series of stages • Calibration + tracking during intervention

32. Other data to register: vectors and tensors http://spl.bwh.harvard.edu:8000/pages/ppl/westin/papers/smr97/node4.html Kilner et al Nature 404:759-761 2000

33. Cerebral atrophy: a macroscopic concomitant of neurodegeneration • Alzheimer’s disease: plaques and tangles, dendritic, neuronal, synaptic loss... and atrophy • Advanced disease = widespread severe atrophy • Early disease: overlap with normal aging

34. FLUID REGISTRATION Non-linear, high-dimensional voxel-by-voxel registration. Viscous fluid model preserves topology Regional volume atrophy can be quantified from the match. RIGID FLUID