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Abstract #4785

GPU-accelerated Diffusion MRI Tractography in DIPY

John Kruper1, Mauro Bisson2, Josh Romero2, Massimiliano Fatica2, and Ariel Rokem1
1Psychology, University of Washington, Seattle, WA, United States, 2NVIDIA, Santa Clara, CA, United States

Synopsis

Keywords: Tractography, Diffusion Software, tractography

Motivation: We make large-scale neuroimaging studies more practical by facilitating the generation of massive tractographies. We enable faster iteration over tractography parameters and models in smaller datasets.

Goal(s): We implemented a GPU-accelerated tractography software with a myriad of methods from the Diffusion Imaging in Python (DIPY) software library, including probabilistic tractography and both single-shell/single-tissue and multi-shell/multi-tissue constrained spherical deconvolution (CSD).

Approach: It is written in CUDA C, but with a python interface. It is installable using the pip package manager and mimics the DIPY API.

Results: Using only a single GPU, it is up to 50x faster than equivalent CPU implementations.

Impact: We introduce GPU-accelerated tractography that achieves up to 50x faster performance per GPU. Through integration with the Diffusion Imaging in Python (DIPY) library, it enables large-scale tractography studies that would not be possible otherwise.

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Keywords