Classification-based Glioma Diffusion Modeling

1. Classification-based Glioma Diffusion Modeling Marianne Morris

2. Overview • Introduction • Motivation • Assumptions • Related Work • Framework • Contribution • Results • Conclusions

3. Introduction • Task: Where to irradiate! • What is a glioma? • What is tumour diffusion modeling? • Brain Biology • MRI • Radiotherapy

4. ?? Normal tissue + Occult cells ?? Treated area tumour Task • Goal: Effective radiotherapy of Brain Tumours • determine what region of brain to treat (irradiate) • Problem: • Just targeting visible tumour cells is NOT enough… • Must also kill “(radiologically) occult” cancer cells surrounding tumour ! • Current Approach: • Irradiate 2cm margin around tumour • Not known if • this area contains occult cells • ONLY this area contains occult cells

5. Better Approach • Locate brain tumours from MRI scan • Predict “(radiologically) occult” cancer cells surrounding tumour • predictor learned from earlier MRI data sets • Treat tumour + predicted-occult region • Meaningful as current techniques can zap arbitrary shapes!

6. Underlying assumptions • Occult cells  future tumour growth • Probability of growth of tumour T into adjacent voxel V is determined by • properties of T: growth rate, histology • properties of V: location, intensity, tissue type • Voxel properties are known throughout brain • Uniformity of brain tumour characteristics

7. What is a glioma? • A primary brain tumour that originated from a cell of the nervous system

8. Diffusion Model Tumor

9. Diffusion Model Neighbours Tumor

10. Diffusion Model Tumor

11. Diffusion Model Neighbours Tumor

12. Diffusion Model Tumor

13. Diffusion Model Tumor

14. Diffusion Model Neighbours Tumor

15. Brain Biology

16. MRIMagnetic Resonance Imaging Signal intensity (on image) determined by T1, T2 relaxation times Magnet signal Echo signal detected Time line in minutes 00: T2 scanning 05: T1 scanning 10: contrast 15: T1-contrast scanning Signal reconstructed into image

17. MRI – image views Axial Sagittal Coronal

18. MRI – image types T2 T1 T1-contrast

19. Tissue differentiation on MRI scans

20. MRI – image types T2 T1 T1-contrast

21. T1-Contrast scan (axial) • Tumour is bright white structure • Necrotic region is black structure • dead cells in center of tumour • Edema may surround tumour • swelling of normal tissue

22. Radiotherapy

23. Radiotherapy

24. Current Treatment Region Irradiate everything within 2 cm margin around tumour … includes • Occult cells • Normal cells

25. Better Treatment Region Irradiate • Tumour • Occult cells • Minimal number of normal cells - minimize loss of brain function • Higher dose of radiation – smaller chance of recurrent cancer Radiotherapy can zap arbitrary shapes!

26. Overview • Introduction • Related Work • Framework • Contribution • Results • Conclusions

27. Related work • Modeling macroscopic glioma growth • 3D cellular automata (Kansal et al., 2000) • Differential motility in grey vs. white matter (Swanson et al., 2002) • White matter tract invasion (Clatz et al., 2004) • Supervised treatment planning (Zizzari, 2004)

28. Related work • 3D cellular automata • Describes the transition of cells within the tumour from dividing to necrotic • Does not assume uniform radial growth • Does not account for biological factors • Too simple to model real tumour growth Proliferating Inactive Necrotic Kansal et al., 2000

29. Related work • A 5:1 ratio in white vs. grey matter Rate of change of tumour cell density = Diffusion of tumour cells + Growth of tumour Dw = 5 Dg Swanson et al., 2000

30. Related work • White matter tract invasion – DTI* • Uses anatomical atlas of white fibers • Initiates simulation from a tumour at time 1 • Uses diffusion-reaction equation • Evaluates results against tumour at time 2 • Only one test patient (GBM) *Diffusion Tensor Imaging Clatz et al., 2004

31. Related work • Modeling macroscopic GBM growth • Differential equations; diffusion-reaction • Supervised treatment planning • Predicts treatment volume using ANN • Trains on control points in predicted clinical volume vs. truth treatment volume • Does not consider brain or patient info Zizzari, 2004

32. Overview • Introduction • Related Work • Framework • Contribution • Results • Conclusions

33. Framework • Noise reduction • Spatial registration • Intensity Standardization • Tissue segmentation • Tumour segmentation • Feature extraction • Classification • Tumour growth modeling Preprocessing Contribution

34. Framework Feature Extraction Classification Tumour Diffusion Modeling Noise Reduction Spatial Registration Intensity Standardization Tissue Segmentation Tumour Segmentation Preprocessing Contribution

35. Framework • Noise reduction • Spatial registration • Intensity Standardization • Tissue segmentation • Tumour segmentation • Feature extraction • Classification • Tumour growth modeling

36. Noise reduction • Inter-slice intensity variation reduction • Reduction of sudden changes in intensity values across the slices of a scan • Using Weighted Linear Regression • Intensity inhomogeneity reduction • Reduction of a varying spatial field across the scan – inherent to MR imaging • Using Statistical Parametric Mapping

37. Inter-slice intensity variation Before inter-slice intensity variation reduction After inter-slice intensity variation reduction

38. Framework • Noise reduction • Spatial registration • Intensity Standardization • Tissue segmentation • Tumour segmentation • Feature extraction • Classification • Tumour growth modeling

39. Spatial registration • Using Statistical Parametric Mapping* • Linear template registration • Registering to same coordinate system • Non-linear warping • Applying deformations to lineup to template • Spatial interpolation • Filling inter-slice gaps and computing intensities *Algorithms specifically designed for the analysis and processing of MRI brain scans

40. Spatial registration Template example Average T2 template Colin Holmes template

41. Spatial registration Before registration After registration

42. Framework • Noise reduction • Spatial registration • Intensity Standardization • Tissue segmentation • Tumour segmentation • Feature extraction • Classification • Tumour growth modeling

43. Intensity Standardization • Reduction of intensity variations across scans • Using Weighted Linear Regression

44. Intensity Standardization Before intensity standardization After intensity standardization

45. Framework • Noise reduction • Spatial registration • Intensity Standardization • Tissue segmentation • Tumour segmentation • Feature extraction • Classification • Tumour growth modeling

46. Tissue segmentation Cerebrospinal fluid Grey matter White matter Using Statistical Parametric Mapping

47. Framework • Noise reduction • Spatial registration • Intensity Standardization • Tissue segmentation • Tumour segmentation • Feature extraction • Classification • Tumour growth modeling

48. Tumour segmentation Slice from patient’s scan Segmented tumour Tumour contour drawn by human experts

49. Framework • Noise reduction • Spatial registration • Intensity Standardization • Tissue segmentation • Tumour segmentation • Feature extraction • Classification • Tumour growth modeling Contribution

50. Features tumour • Patient features • Tumour properties • Voxel features • Neighbourhood attributes A total of 76 features voxel patient