Abstract #2444

Accelerating Diffusion Tensor Imaging of the Rat Brain using Deep Learning

Ali Bilgin^1,2,3,4, Loi Do¹, Phillip A Martin², Ethan Lockhart⁴, Adam S Bernstein¹, Chidi Ugonna¹, Laurel Dieckhaus¹, Courtney Comrie¹, Elizabeth B Hutchinson¹, Nan-Kuei Chen¹, Gene E Alexander^5,6, Carol A Barnes^5,7,8, and Theodore P Trouard^1,3,5

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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