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Abstract #4300

GPU-accelerated diffusion MRI tractography in DIPY

Ariel Rokem1, Mauro Bisson2, Josh Romero2, Thorsten Kurth2, Massimiliano Fatica2, Pablo Damasceno3, Xihe Xie3, Adam Richie-Halford4, Serge Koudoro5, and Eleftherios Garyfallidis5
1Psychology, University of Washington, Seattle, WA, United States, 2NVIDIA, Menlo Park, CA, United States, 3University of California, San Francisco, San Francisco, CA, United States, 4University of Washington, Seattle, WA, United States, 5Indiana University, Bloomington, IN, United States

Tractography based on diffusion-weighted MRI provides non-invasive in vivo estimates of trajectories of long-range brain connections. These estimates are important in research that measures individual differences in brain connections and in clinical use-cases. But the computational demands of tractography present a barrier to progress. Here, we present a GPU-based tractography implementation that accelerates tractography algorithms implemented as part of the Diffusion Imaging in Python (DIPY) project. This implementation speeds up tractography by at least a factor of ~200X, providing tractographies that closely match CPU-based solutions. These speedups enable applications of tractography in clinical data, and in very large datasets.

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