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Registration Foundations

Registration Foundations. Bring multiple image data sets into anatomical agreement. The Registration Problem. T init. T k. T final. Provided by Lilla Zollei. Applications. multi-modality fusion (same patient?) time-series processing e.g.: MS, f MRI experiments, cardiac ultrasound

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Registration Foundations

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  1. Registration Foundations • Bring multiple image data sets into anatomical agreement

  2. The Registration Problem Tinit . . . Tk . . . Tfinal Provided by Lilla Zollei

  3. Applications • multi-modality fusion (same patient?) • time-series processing • e.g.: MS, fMRI experiments, cardiac ultrasound • warping across patients to atlas for labeling • accommodate tissue deformations in image-guided surgery • image-guided surgery of organs other than head

  4. Manual Registration • Not too bad with a few data sets • Re-Position one data set for visual agreement

  5. Automated Medical Image Registration Medical image data sets Transform (move around) Compare with objective function motion parameters score initial value Optimization algorithm Provided by Lilla Zollei

  6. Estimate Relationship Among two Signals • U: a signal • V : another signal, transformed by

  7. Estimate Relationship Among two Signals • If p(U,V) is Gaussian • Then best f is correlation (or squared difference)

  8. Estimate Relationship Among two Signals • If p(U,V) is UNKNOWN • Look for strongest statistical relationship among the signals I : Mutual Information

  9. Mutual Information (MI) • H: entropy • measures information content • I : Mutual Information - a statistic that measures lack of statistical independence

  10. MI Registration • Default Method for Multi-Modal Medical Image Registration • Viola Wells et al. circa 96 • Collignon, and Hill & Hawkes • Pluim et al. Survey, 2003: More than 160 published applications

  11. Example MRT Rigid Registration Pre-operative SPGR MRI Intra-operative T2-weighted MRI Provided by D. Gering

  12. Example MRT Rigid Registration After Registration Before Registration Provided by D. Gering

  13. Real 3D CT data

  14. 3D MR data

  15. “Real” CT-MR registration: 3D starting position

  16. CT-MR registration final result

  17. The end.

  18. 3D Slicer Design • Cross-platform • Built on VTK • Open source platform for visualization • GE, industrial strength • C++, Tck/TK GUI • Open GL • Library interface to graphics hardware • Easily extended • Open source • Available free: www.slicer.org

  19. EM-Segmentation E-Step Compute tissue posteriors using current intensity correction. Estimate intensity correction using residuals based on current posteriors. M-Step Provided by T Kapur

  20. EM Segmentation… Seg Result w/o EM Seg Result With EM PD, T2 Data

  21. EM Segmentation: MS Example PD T2 Data provided by Charles Guttmann

  22. EM Segmentation: MS Example Seg w/o EM Seg with EM

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