Abstract #3344

Deep Learned Diffusion Tensor Imaging

Hongyu Li¹, Chaoyi Zhang¹, Zifei Liang², Dong Liang³, Bowen Shen⁴, Yulin Ge², Jiangyang Zhang², Ruiying Liu¹, Peizhou Huang¹, Sunil Kumar Gaire¹, Xiaoliang Zhang¹, and Leslie Ying¹

¹Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States, ²Radiology, New York University School of Medicine, New York City, NY, United States, ³Paul C. Lauterbur Research Center for Biomedical Imaging, Medical AI research center, SIAT, CAS, Shenzhen, China, ⁴Department of Computer Science, Virginia Tech, Blacksburg, VA, United States

Diffusion tensor imaging typically requires acquisition of a large number of diffusion weighted images (DWI) for accurate fitting of the tensor model due to the issue of low SNR. This abstract presents a deep learning method to generate FA color map showing the primary diffusion directions from very few DWIs. The method uses deep convolutional neural networks to learn the nonlinear relationship between the DWIs and the FA color maps, bypassing the conventional DTI models. Experimental results show that the proposed method is able to generate FA color maps from only 6 DWIs with quality comparable to results from 270 DWIs using conventional tensor fitting.

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