Gradient non-linearities affects diffusion weighted imaging (DWI) as it can result in geometric distortions and spatially varying b-values and gradient directions. The effect is more severe at high gradient strengths. Spherical deconvolution, in particular, relies on a spherical sampling of q-space, which might be affected due to gradient nonlinearities. In this work, we explored the sensitivity of two widely used spherical deconvolution approaches to the gradient non-linearity effect by investigating FOD peak orientation deviations, and evaluate a modified version of DRL that can take into account spatially varying diffusion gradients and weighting. Monte-Carlo simulations and two datasets from the HCP project were used for evaluation.
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