1. Image Guided Surgery in Prostate Brachytherapy Rohit Saboo

2. Prostate Cancer A growing problem in US and world over with increasing longevity Methods of treatment Surgery Irradiation Many problems of existing methods

3. Where is it?

4. Brachytherapy procedure Localized and prolonged dose Brachytherapy overview

5. Brachytherapy - old way Pre-planning CT Outline prostate Develop plan for needle movement Guide needle at time of surgery with help from Ultra sound images “Dynamic Brachytherapy of the prostate under active image guidance”, Gang Cheng et. al, MICCAI 2001

6. Brachytherapy – old way Problems Prostate movement Prostate size/shape variance Due to anesthesia effects Time and hormonal therapy Drawbacks: Lots of error due to prostate movement More than necessary needle insertions

7. Brachytherapy – new way General outline of prostate from pre- planning CT Outline prostate in real-time during surgery Provide guidance for the needle with real time prostate outlining. Track needle errors in real time

8. Steps in automation Acquire volumetric ultra-sound images on the fly Automatically recognize the prostate/rectum and other structures (segmentation) Analyze dose distribution

9. Segmentation The process of outlining the prostate (or any organ) is called segmentation Two chief ways to deal with it Model-based Image based

10. Segmentation problem

11. Ultrasound problems Noise! Speed of sound is not uniform Image distance incorrect in one axis

12. Approaches to segmentation Model based Shape model Probable shapes Probable intensity/texture variations Examples: ASM, AAM, M-reps Image based Outline drawn by expert on one image (atlas) Image intensity/feature based registration Outline carried over

13. Feature Model – Ruo et. al Set of boundary points i - Sample object X i – Tuple representing i th object Each object has m points on the boundary

14. Feature Model X i = (L i, r i1, r i2, … r im ) T

15. Shape variation Mean shape Covariance matrix

16. Feature Model Eigenvalue decomposition of covariance matrix p i Principal components (eigenvectors) Eigen-values Sort the eigenvalues, choose the largest t

17. Eigenvalues

18. New plausible models

19. Optimization - GA

20. Image match Fitness function out i and in i average of intensities along a profile (15 pixels long)

21. Image Match Simplify and speed it up v i unit normal

22. GA parameters

23. 90% crossover rate 1% mutation rate population size 200 2000 generations repeated 15 times

24. Experiment 40 images training from 27, 3 poor quality test on remaining 10 Expert segmentation by two different experts human-human disagreement vs automated-human disagreement

25. Results

29. Summary Very good analysis Point based boundary models are poor Parameter tuning No reasoning for fitness function

30. Methods Model based methods Image based methods Fully automatic, registration Deformable registration – Wei, 2004 Snakes

31. Image based Overview MRI/MRSI and US data prostate carefully outlined on MRI data US image is acquired during operation time The two images are brought into alignment during operation time. They do for biopsy, but same techniques work for brachytherapy

32. Global Alignment US data is poor Model is pre-segmented in MRI Surface to volume alignment methods are used Gradient of image is computed in US and model information is used to roughly find the correspoding boundary in US

33. Global alignment GA based optimization fitness function

34. Registration Deformation is elastic Two orthogonal directions Therefore 2-parameter model for deformation

35. Registration Obtain Curvature images Obtain mapping g for a few points Use these points to drive a TPS deformation.

36. Validation Phantom Gelatin made prostate phantom 15 fiducial markers implanted inside the prostate Soft container filled with water placed on top to simulate pubic bone.

37. Validation

38. Results on phantom

39. Patient study

40. Patient Study

41. Summary Two step registration technique Phantom Studies Only tested over one patient

42. Methods Model based methods Image based methods Fully automatic, registration Deformable registration – Wei, 2004 Snakes semi-automatic In between both

43. Snakes Give an initial approximate contour Two forces act on a snake Internal force Based on curvature External force Based on image gradients Let the model evolve using ordinary force equations till equilibrium

44. Snake based methods Approach used by Zhouping Wei, 2005

45. Snake based methods 1.19 +/- 0.14 mm on average

46. Questions?

47. References “Dynamic Brachytherapy of the prostate under active image guidance”, Gang Cheng et. al, MICCAI, 2001 “Automatic Prostate Boundary Recognition in Sonographic Images Using Feature Model and Genetic Algorithm”, Ruo Yun et. al, Journal of Ultrasound in Medicine, Vol 19, Issue 11, 2000 “Deformable Registration Between MRI/MRSI and Ultrasound Images for Targeted Robotic Prostate Biopsy”, Wei Shao et. al, Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems “A Discrete Dynamic Contour Model”, Steven Lobregt et. al, IEEE Transactions on Medical Imaging, vol 14, no 1, March 1995 “Dynamic Intraoperative Prostate Brachytherapy Using 3D TRUS Guidance with Robot Assistance”, Zhouping Wei et. al, Proceedings of the 2004 IEEE Engineering in Medicine and Biology, 2005