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
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)
Example MR Phantom Images Sagittal Plane Coronal Plane Axial Plane
Bad Slides Partial Phantom No Phantom Off Center Phantom
Finding Edges
Disconnects
Remove Extraneous Features
Can We Fix it? Air Bubble Leaky Slice
Example Edge Images Axial Plane Coronal Plane Sagittal Plane
Midplanes Calculate Midpoints Fit Midplane Ideal shape, size, and orientation of phantom’s faces Stack Midpoints
Ideal Planes Shift ±½ the phantom dimension Perpendicular to face
Distortion Modeling Magnetic Field of Cylinder Sum of spherical harmonics:
Distortion Modeling Measured Corrected
Applying Distortion Correction Axial Plane
Applying Distortion Correction Coronal Plane
Applying Distortion Correction Sagittal Plane
Results Theoretical undistorted points vs. corrected points Standard deviations of correction +X face: Standard Deviation = mm -X face: Standard Deviation = mm +Y face: Standard Deviation = mm -Y face: Standard Deviation = mm +Z face: Standard Deviation = mm -Z face: Standard Deviation = mm
Conclusions 3 range 0.4 – 0.8 mm – –1-2 pixels on each image – –Originally 2mm (5-6 pixels) Accurate localization of anatomical targets
Future Work Further verification and testing Clinical trials FDA approval Treatment on humans