Software Development For Correction of Gradient Nonlinearity Distortions

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

Example MR Phantom Images Axial Plane Coronal Plane Sagittal Plane

Partial Phantom No Phantom Off Center Phantom Bad Slides

Finding Edges

Disconnects

Remove Extraneous Features

Leaky Slice Air Bubble Can We Fix it?

Example Edge Images Axial Plane Coronal Plane Sagittal Plane

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

Ideal Planes n Shift ±½ the phantom dimension n Perpendicular to face

Distortion Modeling n Magnetic Field of Cylinder n Sum of spherical harmonics:

Distortion Modeling Measured Corrected

Applying Distortion Correction Axial Plane

Applying Distortion Correction Coronal Plane

Applying Distortion Correction Sagittal Plane

Results n Theoretical undistorted points vs. corrected points n 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

Conclusions n 3 s range 0. 4 – 0. 8 mm – 1 -2 pixels on each image –Originally 2 mm (5 -6 pixels) n. Accurate localization of anatomical targets

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