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Abstract #1291

Connecting Histology and MRI using Deep Learning

Zifei Liang1, Choong Heon Lee1, Tanzil M. Arefin1, Piotr Walczak2, Song-Hai Shi3, Florian Knoll1, Yulin Ge1, Leslie Ying4, and Jiangyang Zhang1
1Radiology, NYU Langone Health, New York, NY, United States, 2Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, 3Center for Molecular Imaging & Nanotechnology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 4Electrical Engineering, University at Buffalo, Buffalo, NY, United States

We developed a deep learning network that can generate new tissue contrasts from MRI data to match the contrasts of several histological methods. The network was trained using the carefully curated histological data from the Allen Institute mouse brain atlas and co-registered MRI data. In our tests, the new contrasts, which resembled Nissl, neurofilament, and myelin-basic-protein stained histology, demonstrated higher sensitivity and specificity than commonly used diffusion MRI markers to characterize neuronal, axonal, and myelin structures in the mouse brain. The contrasts were further validated using two mouse models with abnormal neuronal structures and dysmyelination.

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