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Abstract #0969

DeepDTI: Six-direction diffusion tensor MRI using deep learning

Qiyuan Tian1,2, Berkin Bilgic1,2, Qiuyun Fan1,2, Congyu Liao1,2, Chanon Ngamsombat1, Yuxin Hu3, Thomas Witzel1, Kawin Setsompop1,2, Jonathan R. Polimeni1,2, and Susie Y. Huang1,2
1Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Department of Electrical Engineering, Stanford University, Stanford, CA, United States

Diffusion tensor imaging (DTI) is widely used clinically but typically requires acquiring diffusion-weighted images (DWIs) along many diffusion-encoding directions for robust model fitting, resulting in lengthy acquisitions. Here, we propose a joint denoising and q-space angular super-resolution method called “DeepDTI” achieved using data-driven supervised deep learning that minimizes the data requirement for DTI to the theoretical minimum of one b=0 image and six DWIs. Metrics derived from DeepDTI’s results are equivalent to those obtained from three b=0 and 19 to 26 DWI volumes for different scalar and orientational DTI metrics, and superior to those derived from state-of-the-art denoising methods.

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