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Image Registration Techniques, Benchmarking, Strategy

Image Registration Techniques, Benchmarking, Strategy. Surgical Planning Laboratory Center for Neurological Imaging. July 2010 Lidwien Veugen Supervision by Dominik S. Meier, PhD. Contents. - Introduction Image Registration, 3D Slicer - Theory Transformations, Similarity Metrics

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Image Registration Techniques, Benchmarking, Strategy

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  1. Image RegistrationTechniques, Benchmarking, Strategy • Surgical Planning Laboratory • Center for Neurological Imaging • July 2010 • Lidwien Veugen • Supervision by Dominik S. Meier, PhD

  2. Contents • - Introduction • Image Registration, 3D Slicer • - Theory • Transformations, Similarity Metrics • - Benchmarking • Time/Memory vs Iterations/Samples • - Registration Strategies • - Registration Cases • Brains, PET-CT, EMPIRE10

  3. Introduction • Image Registration: • - Process of matching multiple image by optimal transformation • 3D Slicer: • - FreeOpen Source Software program • - Huge amount of Registration Modules/Methods

  4. 3D Slicer

  5. Theory Transformations • Mapping points from original spatial coordinates to new spatial coordinates: (u,v,w) = T{(x,y,z)} • Rigid Transform • Rotation + Translation • (u,v,w) = R*(x,y,z) + t • 6 DOF • Affine Transform • Rotation + Translation + Scaling + Shear • (u,v,w) = A*(x,y,z) + t • 12 DOF

  6. Theory Transformations • BSpline • Spline: function defined piecewice by polynomials • Cubic grid of moving control points describes deformation • 3 DOF per control point • BrainsDemonWarp • Thirion + Maxwell: Image registration based on optical flow • Boundaries are semi-permeable membranes with effectors/demons • High DOF

  7. Theory Transformation • BRAINSFit • - Rigid, Affine, BSpline • - Mutual Information • - 6/12/higher DOF • Plastimatch • - Pipeline: Rigid/Affine, BSpline(s) • - MutualInfo + MeanSqE • - 6/12/higher DOF • Expert Automated Registration • - Pipelines: Rigid, Affine, BSpline • - MutualInfo + MeanSqE + NormCorr • - 6/12/higher DOF

  8. Theory Similarity Metrics • Tells to what degree two images are aligned • Based on: intensity, landmarks • Mutual Information • - Measure of the statistical dependence between two random variables: • Information about image A that is shared by B and vice versa • - Maximized if the two images are spatially aligned • - Based on Shannon entropy H: measure of intensity prediction • - Fast measure

  9. Theory Similarity Metrics • Mean Squared Difference • - Summation of the squared differences between two images • - Minimized if the two images are spatially aligned • - Intra-patient + Intra-modality • - Time consuming • Normalized Cross Correlation • - Based on cross correlation • - Maximized if the two images are spatially aligned • - Intra-patient + Intra-modality • - Time consuming

  10. Theory Optimization • Optimization algorithm: • Tries to find a global solution to an energy function • - Gradient descent • - Statistical optimization • - Line search algorithm • - One-plus-one evolutionary • - Multiresolution

  11. Registration Accuracy • Subtraction • Fixed - MovingRegistered • Checkerboard • Alternating squares from • fixed and moving image

  12. Benchmarking • Effect of the amount of iterations and samples on CPU time and memory for different modules/methods • Rigid: 4 methods • Affine: 7 methods • BSpline: 2 methods • Default: Samples = 10000, Iterations = 200 • Iterations: 11 values, ranging from 25 to 20 000 • Samples: 20 values, ranging from 25 to 10 000 000

  13. Benchmarking • Fast results with: • SPL Dell Linux Cluster of 50 computers •  Creates log-file of every job  Matlab

  14. Benchmarking Results

  15. Benchmarking Results • Time vs Samples • - Increase: All modules, except: • - Decrease: Exp.Autom. NormCr • - 10  800 seconds (0.003%  13%) • - Rigid < Affine < BSpline • Time vs Iterations • - Not much effect • - Increase: Brainsfit, Exp.Autom. • - Decrease: Multiresolution • - Constant: BSpline modules • Memory vs Iterations • Not much effect • - Increase: All modules, except: • - Constant: Brainsfit, Multires • - Lowest: 2MB; Highest: 155MB • Memory vs Samples • - Increase: All modules, except: • - Decrease: Exp.Autom. NormCr • - Lowest: Rigid (10-100MB) • - Highest: BSpline (400-1300MB)

  16. Registration Cases • Slicer Registration Case Library

  17. Registration Strategies • Choice of Transformation • Modality, Subject, Inter/Intra, Part of body • Choice of Similarity Metric • Inter/Intra, Time/Accuracy • Focus • Time/Accuracy/Memory  Sim.metric/iterations/samples • Fixed Image • Resolution/Contrast

  18. Registration Cases • EMPIRE10 • Evaluation of Methods for Pulmonary Image Registration 2010 • = Challenge of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) • 20 Pairs of chest CT scans: variety scanners, voxel size, breathing phase • Evaluation: Lung boundaries, Fissures, Landmarks, Singularities

  19. Registration Cases

  20. Registration Cases • EMPIRE10 - Registration Pipeline: • Fast Affine Registration • Fast nonrigid Bspline Registration (grid = 7) • Fast nonrigid Bspline Registration (grid = 12) • Fast nonrigid Bspline Registration (grid = 17) • BrainsDemonWarp

  21. Registration Cases • EMPIRE10 - Quality Registration • Subtraction + MATLAB help in evaluation registration: • Median pixelvalue of absolute subtracted image: the lower the better

  22. Registration Cases • fMRI alignment to structural scan (T1) • - Fixed: T1 scan (anatomical reference) • - Moving: fMRI scan • Problem: Low tissue contrast, acquisition related distortions • Registration based on ventricles only

  23. Registration Cases • Aging Mobility Study 2 year follow-up • - 2 Exams at different times: nonrigid (BSpline) • - Incorrect axis-info • - Fixed: MPRAGE • Moving: T2, FLAIR

  24. Registration Cases • Inter-subject Normal brain MIDASexample • - Fixed: T1 • Moving: T2, MRA • Interpatient: non-rigid (BSpline)

  25. Registration Cases • PET-CT Fusion 2- Intersubject: nonrigid • BSpline, BrainsDemonWarp • Fixed: CT-scan patient 1 • Moving: CT-scan patient 2 • Problem: Different posture

  26. Registration Cases • Brain Intersubject PNL-XNAT • Intersubject: nonrigid (BSpline) • Problems with (too much) BSpline

  27. Registration Cases • Brain Intersubject OrientationFlx • Intersubject: nonrigid (BSpline) • Fixed: T1 • Moving: T2 • Problems with nested transformations

  28. Registration Cases • Brain Intersubject Dartmouth • Montreal Neurological Institue: • Colin27 for group analysis in MRI studies • - Fixed: Colin27 • Moving: Patient • Orientation!

  29. Acknowledgements • Finally, I would like to thank everybody from CNI for the possibility to do an internship here! • Thanks to my supervisor Dominik S. Meier, PhD

  30. Questions? • ?

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