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Abstract #2444

Accelerating Diffusion Tensor Imaging of the Rat Brain using Deep Learning

Ali Bilgin1,2,3,4, Loi Do1, Phillip A Martin2, Ethan Lockhart4, Adam S Bernstein1, Chidi Ugonna1, Laurel Dieckhaus1, Courtney Comrie1, Elizabeth B Hutchinson1, Nan-Kuei Chen1, Gene E Alexander5,6, Carol A Barnes5,7,8, and Theodore P Trouard1,3,5
1Biomedical Engineering, University of Arizona, Tucson, AZ, United States, 2Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, 3Medical Imaging, University of Arizona, Tucson, AZ, United States, 4Program in Applied Mathematics, University of Arizona, Tucson, AZ, United States, 5Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States, 6Departments of Psychology and Psychiatry, University of Arizona, Tucson, AZ, United States, 7Division of Neural System, Memory & Aging, University of Arizona, Tucson, AZ, United States, 8Departments of Psychology, Neurology and Neuroscience, University of Arizona, Tucson, AZ, United States

The aim of this work is to accelerate analysis of diffusion weighted MRI (dMRI) of the rat brain using deep learning. The proposed approach allows prediction of unacquired diffusion-weighted images (DWIs) from a small set of acquired DWIs. By combining the acquired and predicted DWIs, accurate and reliable diffusion tensor metrics can be obtained with up to ten-fold reduction in scan time.

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