1. Software Development For Correction of Gradient- Nonlinearity Distortions in MR Images T.S. Lee, K.E. Schubert Computer Science CSUSB R.W. Schulte Radiation Medicine LLUMC

2. Functional Proton Radiosurgery Functional Neurosurgery – –Trigeminal Neuralgia –Parkinson’s Disease – –Brain regions (< 1 cm) Proton Radiosurgery – –Accurate to less than 1 mm MRI for target localization – –Distinguish tissue types – –512  512 images – –262,144 pixels/study – –Gradient nonlinearity distortions (~2mm)

3. Example MR Phantom Images Sagittal Plane Coronal Plane Axial Plane

4. Bad Slides Partial Phantom No Phantom Off Center Phantom

5. Finding Edges

6. Disconnects

7. Remove Extraneous Features

8. Can We Fix it? Air Bubble Leaky Slice

9. Example Edge Images Axial Plane Coronal Plane Sagittal Plane

10. Midplanes Calculate Midpoints Fit Midplane Ideal shape, size, and orientation of phantom’s faces Stack Midpoints

11. Ideal Planes Shift ±½ the phantom dimension Perpendicular to face

12. Distortion Modeling Magnetic Field of Cylinder Sum of spherical harmonics:

13. Distortion Modeling Measured Corrected

14. Applying Distortion Correction Axial Plane

15. Applying Distortion Correction Coronal Plane

16. Applying Distortion Correction Sagittal Plane

17. Results Theoretical undistorted points vs. corrected points Standard deviations of correction +X face: Standard Deviation = 0.23503 mm -X face: Standard Deviation = 0.25168 mm +Y face: Standard Deviation = 0.15322 mm -Y face: Standard Deviation = 0.15823 mm +Z face: Standard Deviation = 0.13548 mm -Z face: Standard Deviation = 0.14173 mm

18. Conclusions 3  range 0.4 – 0.8 mm – –1-2 pixels on each image – –Originally 2mm (5-6 pixels) Accurate localization of anatomical targets

19. Future Work Further verification and testing Clinical trials FDA approval Treatment on humans