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Abstract #0550

Gradient Non-Linearity Correction for Spherical Mean Diffusion Imaging

Michael Paquette1, Cornelius Eichner1, and Alfred Anwander1

1Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Gradient non-linearities are a significant source of errors in MRI systems with strong gradients. In the case of diffusion imaging, they induce spatial deviation of the b-vectors. The spherical mean methods in diffusion relies on the acquisition of spherical b-shell. To recover accurate spherical mean values, it is necessary to undistort the diffusion signal. Therefore, we evaluated three correction methods for gradient non-linearities using the Connectom gradient system as a showcase. We show how a simple heuristic can reduce the spherical mean errors by 20 folds.

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