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Abstract #4474

Mapping white matter specificity captured by diffusion tractography through deep learning on structural MRI

Qi Yang1, Colin Hansen1, Francois Rheault2, Bramsh Qamar3, Owen Williams4, Susan Resnick4, Eleftherios Garyfallidis3, Adam W Anderson5,6, Maxime Descoteaux2, Bennett A Landman5,6,7,8, and Kurt G Schilling5
1Computer Science, Vanderbilt University, Nashville, TN, United States, 2Sherbrooke Connectivity Imaging Laboratory (SCIL), Universite de Sherbrooke, Sherbrooke, QC, Canada, 3Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States, 4Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, United States, 5Vanderbilt University Institute of Imaging Science, Nashville, TN, United States, 6Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, 7Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, 8Electrical Engineering, Vanderbilt University, Nashville, TN, United States

We investigate the value added by diffusion fiber tractography dissection of white matter pathways over standard T1w images in which WM is largely homogenous. We analyze structural differences in pathway segmentations between a deep learning network trained to label WM directly on T1w images and the same pathways dissected using tractography, and find that although the core of the stem of many fiber bundles is accurately delineated, the value of tractography is in delineating terminal connections and determining fine-scale stem geometrical properties. Thus, while localizing regions-of-interest is possible in structural images, tractography is needed for increased pathway and connection information.

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