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  1. Medical Image Registration J. Michael Fitzpatrick, Department of Electrical Engineering and Computer ScienceVanderbilt University, Nashville, TN Course on Medical Image Registration, Nov 3-Nov 24, 2008Institute für Robotic, Leibniz UniversitätHannover, Germany

  2. Schedule Nov 3: Overview of Medical Image Registration Nov 10: Point-based, rigid registration Nov 17: Intensity-based registration Nov 24: Non-rigid registration

  3. Computed Tomography (1972) Siemens CT Scanner (Somatom AR)

  4. 3D Cross-sectional Image “voxels” (“volume elements”)

  5. Magnetic Resonance Imaging GE MR Scanner (Signa 1.5T)

  6. Positron Emission Tomography GE PET Scanner

  7. Physician has 3 or more views. MR (wet tissue) PET (biologicalactivity) CT (bone)

  8. Combining multiple images requires image registration

  9. Image Registration: Definition Determination of corresponding points in two different views

  10. Motion relative to the scanners can be three-dimensional.

  11. Slice orientations vary widely. transverse sagittal coronal

  12. Views may be very different.

  13. But all orientations and all views can be combined if we have the 3D point mapping.

  14. MR PET CT CT + MR MR + PET Combining Registered Images = “Image Fusion”

  15. Rigid Registration: Definition Rigid Registration = Registration using a “rigid” transformation

  16. 6 degrees of freedom Rigid Transformation Distances between all points remain constant. Rigid Non-rigid

  17. Nonrigid Transformationscan be very complex! [Thompson, 1996]

  18. Non-rigid example

  19. Registration Dichotomy • “Retrospective” methods (nothing attached to patient before imaging) • Match anatomical features: e.g., surfaces • Maximize similarity of intensity patterns • “Prospective” methods (something attached to patient before imaging) • Non-invasive: Match skin markers • Invasive: Match bone-implanted markers

  20. Most Common Approaches • Intensity-based* (not for surgical guidance) • Surface-based (requires identified surfaces) • Point-based (requires identified points) • Stereotactic frames (for surgical guidance) *Sometimes called “voxel-based”

  21. The Most Successful Intensity-Based Method:Mutual Information

  22. MR intensity CT intensity MR CT 2D Intensity Histogram (Hill94)

  23. Misregistration Blurs It 5 cm 0 cm 2 cm MR CT MR PET Hill, 1994

  24. Mutual Information(Viola, Collignon, 1996) • A measure of histogram sharpness • Most popular “intensity” method • Assumes a search method is available • Stochastic, multiresolution search common • Requires a good starting pose • May not find global optimum • Not useful for surgical guidance

  25. Example: Mutual Information Studholme, Hill, Hawkes, 1996, “Automated 3D registration of MR and CT images of the head”, MIA, 1996 (Open movie with QuickTime)

  26. The Most Successful Surface-Based Method:The Iterative Closest-Point Algorithm

  27. Iterative Closest-Point Method(Besl and McKay, 1992) • Minimizes a positive distance function • Assumes surfaces have been delineated • Guaranteed to converge • Requires a good starting pose • May not find global optimum • Can be used for surgical guidance

  28. Start with two surfaces

  29. Reorient one (somehow)

  30. Reorient one (somehow)

  31. Reorient one (somehow)

  32. Pick points on moving surface

  33. Pick points on moving surface

  34. Remove moving surface

  35. Points become proxy for surface

  36. Find closest points on stationary surface

  37. Measure the total distance

  38. Remove stationary surface

  39. Points become proxy for surface

  40. Register point sets (rigid)

  41. Register point sets (rigid)

  42. Restore stationary surface

  43. Find (new) closest points

  44. Find (new) closest points

  45. Remove stationary surface

  46. Remove stationary surface

  47. Register Points

  48. Register Points, and so on…

  49. Iterative Closest-Point Algorithm: • Find closest points • Measure total distance • Register points Stop when distance change is small.

  50. ICP: Image-to-Image Dawant et al.