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Abstract #2055

Fast implementations of contextual PDE’s for HARDI data processing in DIPY

Stephan Meesters1, Gonzalo Sanguinetti1, Eleftherios Garyfallidis2, Jorg Portegies1, and Remco Duits1

1Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands, 2Computer Science Department, University of Sherbrooke, Sherbrooke, QC, Canada

We present a novel open-source module that implements a contextual PDE framework for processing HARDI data. It’s potential in enhancement of ODF/FOD fields is demonstrated where the aim is to enhance the alignment of elongated structures while preserving crossings. The method for contextual enhancement is based on a hypo-elliptic PDE defined in the domain of coupled positions and orientations and can be solved with a shift-twist convolution. The module is available in the DIPY (Diffusion Imaging in Python) software library, which makes it widely available for the neuroimaging community.

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