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Abstract #0080

TORTOISE V4: ReImagining the NIH Diffusion MRI Processing Pipeline

M. Okan Irfanoglu1, Amritha Nayak1,2, Paul Taylor3, and Carlo Pierpaoli1
1QMI/NIBIB, National Institutes of Health, Bethesda, MD, United States, 2Henry Jackson Foundation, Bethesda, MD, United States, 3Scientific and Statistical Computing Core, NIMH, National Institutes of Health, Bethesda, MD, United States

Synopsis

Keywords: Data Processing, Diffusion/other diffusion imaging techniquesThe processing needs for diffusion MRI data have evolved over the years with data sizes getting larger, diffusion sensitization going higher. Large multi-site studies, especially on "uncooperative subjects" such as young children or patients with movement disorders increased the necessity for dMRI processing pipelines that are fast, robustly capable of handling a variety of artifacts/distortions, and that have summary reporting capabilities that can pinpoint problematic data. The NIH Diffusion MRI processing pipeline, TORTOISE, has been reimagined, redesigned and and significantly enriched to satisfy these processing needs.

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