Abstract #4422

Six-direction diffusion tensor MRI using a convolutional neural network

Qiyuan Tian¹, Berkin Bilgic^1,2, Qiuyun Fan¹, Chanon Ngamsombat¹, Congyu Liao¹, Yuxin Hu³, Thomas Witzel¹, Kawin Setsompop^1,2, Jonathan R Polimeni^1,2, and Susie Y Huang^1,2

¹Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States, ²Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States

Diffusion tensor imaging (DTI) is widely used for clinical neuroimaging and neuroscientific research but has traditionally suffered from relatively length acquisition. Here, we propose a new approach to obtain both scalar and orientational DTI metrics from six diffusion-weighted images with optimal directional encoding. Through the careful choice of diffusion directions, we compute initial tensor results that are then denoised using a convolutional neural network. Our results provide comparable scalar and orientational DTI metric maps to those acquired with 90 directions.

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