Abstract-Functional radiosurgery is a noninvasive stereotactic technique that requires magnetic resonance image (MRI) sets with high spatial resolution. Gradient nonlinearities introduce geometric distortions that compromise the accuracy of MRI-based stereotactic localization. We present a gradient nonlinearity correction method based on a cubic phantom MRI data set. The approach utilizes a sum of spherical harmonics to model the geometrically warped planes of the cube and applies the model to correct arbitrary image sets acquired with the same scanner. In this paper, we give a detailed description of the Matlab distortion correction program, report on its performance in stereotactic localization of phantom markers, and discuss the possibility to accelerate the code using General-Purpose Computing on Graphics Processing Units (GPGPU) techniques.

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