Abstract #1335

SuperDTI: Superfast Deep-learned Diffusion Tensor Imaging

Hongyu Li¹, Zifei Liang², Chaoyi Zhang¹, Ruiying Liu¹, Jing Li³, Weihong Zhang³, Dong Liang⁴, Bowen Shen⁵, Peizhou Huang⁶, Sunil Kumar Gaire¹, Xiaoliang Zhang⁶, Yulin Ge², Jiangyang Zhang², and Leslie Ying^1,6

¹Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States, ²Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, NY, United States, ³Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China, ⁴Paul C. Lauterbur Research Center for Biomedical Imaging, Medical AI research center, SIAT, CAS, Shenzhen, China, ⁵Computer Science, Virginia Tech, Blacksburg, VA, United States, ⁶Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States

The main factor that prevents diffusion tensor imaging (DTI) from being incorporated in clinical routines is its long acquisition time of a large number of diffusion-weighted images (DWIs) required for reliable tensor estimation. This abstract presents SuperDTI to learn the nonlinear relationship between DWIs (reduced in q-space and k-space) and the corresponding tensor-derived quantitative maps as well as fiber tractography. Experimental results show that the proposed method can generate fractional anisotropy and mean diffusivity maps, as well as fiber tractography, from as few as six undersampled raw DWIs with quality comparable to results from 90 DWIs using conventional tensor fitting.

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