MSc project Janneke Ansems 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 Outline  Introduction  Registration  Materials and Methods  Results  Discussion and Conclusion  Recommendations

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

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

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 Registration Similarity Measure

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 Optimization Gauss-Newton

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

17 Registration Summary Intensity Based

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

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

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

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

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

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

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

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 features  Arun’s algorithm for transformation

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

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

34 Results Table 1.1: Results from the SSD program.

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

36 Results Table 1.2: Results from the NGF program.

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

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

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

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

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

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 Thank you for your attention! Questions?