1. MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr. ir. B.M. ter Haar Romeny Prof. dr. ir. F.N. van de Vosse Dr. ir. B. Platel Dr. ir. G.J. Strijkers

2. 2 Outline  Introduction  Registration  Materials and Methods  Results  Discussion and Conclusion  Recommendations

3. 3 Introduction Medical Background  Brain tumors  Cancer is 2nd major cause of death in the Netherlands at present time  Each year 1000 people in the Netherlands are diagnosed with a brain tumor  Treatment  Radiotherapy  Resection surgery Figure 1: benign (left) and malignant tumor

4. 4 Introduction Image-Guided Surgery  Image-Guided Surgery  The use of images to guide a surgeon during the procedure  Medtronic Stealth Station  Surgeon is able to verify the location of a tumor directly with the images using an image guided probe  But: pre-operative images do not always resemble the real-time situation during surgery! Figure 2: Medtronic Stealth Station

5. 5 Introduction Intra-Operative Imaging  Brain shift  Intra-operative imaging during surgery gives a more accurate view on the real-time situation Figure 3: Axial slices during a craniotomy showing brain shift.

6. 6 Introduction Intra-Operative MRI  The Polestar N20 open intra-operative MR scanner (Medtronic Inc.)  Field strength: 0.15 Tesla  Resolution: 128x128x64  Field of view: 20x20x19 cm  Chosen for its:  Relative low cost  Open access to the patient  Mobility  Local shielding  Compatibility with Medtronic Stealth Station  Compromise: image quality Figure 4: The Polestar N20 system in the operating room.

7. 7 Introduction Image quality Polestar  Due to low field the Polestar scanner is susceptible to noise and artifacts  Intensity Gradient  Distortions Figure 5: Images of Phantoms scanned by the polestar N20 showing distortion (left) and intensity gradient.

8. 8 Introduction Aim  Register 3D pre-operative high resolution MR data and the intra-operative MR data from the Polestar N20 with maximum accuracy.  In this way high resolution accurate information is available for navigational purposes during neurosurgery, focus on datasets with skull intact.

9. 9 Outline  Introduction  Registration  Materials and Methods  Results  Discussion and Conclusion  Recommendations

10. 10 Registration  Definition:  Given a reference image R and a template image T, find a suitable transformation y such that the transformed image T[y] looks similar to the reference image R ReferenceTemplate

11. 11 Registration Transformation Model  Definition:  A mapping of locations of one image to new locations in another image

12. 12 Registration Similarity Measure  Definition:  Equation that measures how much two images are alike  Intensity-based Methods  Sum of Squared Differences:  Gradient-based Methods  Normalized Gradient Field: Reference Template

13. 13 Registration Similarity Measure

14. 14 Registration Optimizing Scheme  Definition:  An optimizing scheme calculates the transformation parameters to achieve maximum similarity  Steepest Descent  Gauss-Newton  Levenberg-Marquardt Figure 6: A comparison of steepest descent (green) and Gauss-Newton's method (red) for minimizing a function

15. 15 Optimization Gauss-Newton

16. 16 Registration Multilevel approach  Definition:  Register from coarse to fine to optimize for speed and robustness

17. 17 Registration Summary Intensity Based

18. 18 Registration Features  Definition:  Features are a finite number of pixels or groups of pixels that are unique and exist in both images.  Given features r 1, …, r n in reference image and t 1, …, t n in template image, find a transformation y such that:

19. 19  Automatic feature detection  Scale Invariant Feature Transform (SIFT) by Lowe  2D version gave promising results Registration Features Figure 6: Matches found by SIFT algorithm in Polestar data (left) and high resolution data

20. 20  Manual feature selection  Transformation using Arun’s algorithm  Demo Brainmark Registration Features

21. 21

22. 22 Outline  Introduction  Registration  Materials and Methods  Results  Discussion and Conclusion  Recommendations

23. 23 Materials and Methods Datasets  Four datasets:  Pre-operative 1.5 Tesla and intra-operative 0.15 Tesla MR data  Two datasets of healthy volunteers  Two partial datasets of patients Figure 7: Mid-sagittal slices of the high and low resolution MR data of a healthy volunteer (left) and patient.

24. 24 Materials and Methods Preprocessing  Initial Alignment  Important for optimization scheme  Gravity point of nonzero voxels in sagittal direction  Gradient removal  Skull removal

25. 25 Materials and Methods Preprocessing  Global Intensity Gradient Removal  Subtract peaks from Gaussian blurred image

26. 26 Materials and Methods Preprocessing  Gradient Removal  Subtract peaks from Gaussian blurred image

27. 27 Materials and Methods Preprocessing  ‘Skull’ stripping  Dilation and erosion of a binary mask

28. 28 Materials and Methods Preprocessing  ‘Skull’ stripping  Dilation and erosion of a binary mask

29. 29 Materials and Methods Registration programs  Intensity-based registration programs:  Rigid transformation model  Sum of Squared Differences and Normalized Gradient Field  Gauss-Newton optimization scheme  Multilevel approach  Feature-based registration program:  Manual selection of 10-15 features  Arun’s algorithm for transformation

30. 30 Materials and Methods Experiments  Intensity-based registrations  Four datasets were registered using the SSD and NGF programs  Multilevel approach: 1, 2 and 3 levels (resolution steps) were used  Feature-based registration  Four datasets were registered  The results will be used as initial parameter guess for the optimizing scheme of the NGF program to register the patient datasets

31. 31 Materials and Methods Visualization  To inspect registration results, a Graphical User Interface (GUI) was built Figure 8: A screenshot of Regview to inspect registration results visually. The green lines indicate the cross-section with the other two views.

32. 32 Materials and Methods Visualization  Three different settings to inspect registration results Figure 9: Checkerboard (left), fusion (middle) and transition visualization.

33. 33 Outline  Introduction  Registration  Materials and Methods  Results  Discussion and Conclusion  Recommendations

34. 34 Results Table 1.1: Results from the SSD program.

35. 35 Results  Influence of resolution steps, VolunteerNeuro002 One resolution step Two resolution steps

36. 36 Results Table 1.2: Results from the NGF program.

37. 37 Results Registration of VolunteerNeuro001 using NGF, two resolution steps:

38. 38 Results Table 1.3: Results from the feature-based program.

39. 39 Results After manual selection of 10 features Using feature based initialization for NGF registration program

40. 40 Results  The intensity-based registration programs managed to register the datasets of the healthy volunteers  However both intensity-based programs were not able to register the partial datasets of the patients without manual initial parameter guess  Best results were obtained by using a feature-based registration as initial parameter guess for the intensity-based programs.

41. 41 Outline  Introduction  Registration  Materials and Methods  Results  Discussion and Conclusion  Recommendations

42. 42 Conclusion and Discussion  All datasets are registered, results were inspected using visual inspection  Preprocessing important for intensity-based programs  Accuracy of the voxelsize is feasible in the center of the field of view  However this accuracy is not attainable at the edge of the field of view due to distortions and artifacts resulting from the low field of the Polestar N20

43. 43 Outline  Introduction  Registration  Materials and Methods  Results  Discussion and Conclusion  Recommendations

44. 44 Recommendations  Image quality Polestar  Currently a phantom is developed to measure and correct the distortion  Next step: registration after skull opening but before tumor resection  Non rigid transformation model  Computation time  Validation

45. 45 Thank you for your attention! Questions?