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Abstract #1209

Estimating susceptibility-induced field changes directly from diffusion MRI images and overcoming associated computational bottlenecks through GPU parallelisation

Frederik Lange1, Mark Graham2, Ivana Drobnjak2, Hui Zhang2, Jon Campbell1, and Jesper Andersson1

1FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 2Centre for Medical Image Computing & Department of Computer Science, University College London, London, United Kingdom

We present a novel method (incorporated into FSL $$$\texttt{eddy}$$$) of estimating and correcting distortions due to dynamic changes in the susceptibility field when measuring diffusion using EPI. This method is able to track how the field changes with movement using only the diffusion data itself. We demonstrate an improvement in distortion correction, compared to using a static susceptibility field, for both simulated and actual data. Additionally, we demonstrate how computationally intensive portions of the estimation algorithm can be speeded up through GPU parallelisation. Reducing the runtime increases the likelihood of this method being widely adopted and broadens its impact potential.

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