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
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
Deep
learning enables calculation of diffusion tensor metrics with up to ten-fold
reduction in scan time when imaging the rat brain.
Figure1: (a) In
conventional DTI, acquired DWIs are first used to estimate the diffusion
tensor. The resulting tensor is used to compute the DTI metrics. (b) In
proposed DL-DTI, the acquired DWIs are first used to predict DWIs that were not
acquired. The acquired and predicted DWIs are used together for estimating the
diffusion tensor. The diffusion tensor is then used to compute the DTI metrics.
Figure 3: Comparison of DTI
metrics. Directionally Encoded Color (DEC), Fractional Anisotropy (FA), Mean Diffusivity
(MD), Axial Diffusivity
(AD), and Radial Diffusivity (RD) maps are shown for
a reference (N=64) dataset together with the same metrics obtained using the
conventional DTI (Figure 1(a)) and DL-DTI (Figure 1(b)) approaches using a varying
number of input DWIs. The metrics obtained from DL-DTI correspond well to those
obtained using the reference dataset even at high acceleration rates.
