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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