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
Schedule Nov 3: Overview of Medical Image Registration Nov 10: Point-based, rigid registration Nov 17: Intensity-based registration Nov 24: Non-rigid registration
Computed Tomography (1972) Siemens CT Scanner (Somatom AR)
3D Cross-sectional Image “voxels” (“volume elements”)
Magnetic Resonance Imaging GE MR Scanner (Signa 1.5T)
Positron Emission Tomography GE PET Scanner
Physician has 3 or more views. MR (wet tissue) PET (biologicalactivity) CT (bone)
Image Registration: Definition Determination of corresponding points in two different views
Slice orientations vary widely. transverse sagittal coronal
But all orientations and all views can be combined if we have the 3D point mapping.
MR PET CT CT + MR MR + PET Combining Registered Images = “Image Fusion”
Rigid Registration: Definition Rigid Registration = Registration using a “rigid” transformation
6 degrees of freedom Rigid Transformation Distances between all points remain constant. Rigid Non-rigid
Nonrigid Transformationscan be very complex! [Thompson, 1996]
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
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”
MR intensity CT intensity MR CT 2D Intensity Histogram (Hill94)
Misregistration Blurs It 5 cm 0 cm 2 cm MR CT MR PET Hill, 1994
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
Example: Mutual Information Studholme, Hill, Hawkes, 1996, “Automated 3D registration of MR and CT images of the head”, MIA, 1996 (Open movie with QuickTime)
The Most Successful Surface-Based Method:The Iterative Closest-Point Algorithm
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
Iterative Closest-Point Algorithm: • Find closest points • Measure total distance • Register points Stop when distance change is small.
ICP: Image-to-Image Dawant et al.