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

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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

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