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Abstract #1591

The influence of gradient nonlinearity on spherical deconvolution approaches: to correct or not to correct?

Fenghua Guo1, Greg Parker2, Alberto De Luca1, Derek Jones2, Max Viergever1, Alexander Leemans1, and Chantal Tax2

1Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, 2Cardiff University Brain Research Imaging Centre, Cardiff, United Kingdom

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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