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Abstract #3985

Feasibility of Multi-contrast MR imaging via deep learning

Shanshan Wang1, Tao Zhao1,2, Ningbo Huang1,3, Sha Tan1,4, Yuanyuan Liu1, Leslie Ying5, and Dong Liang1

1Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen, People's Republic of China, 2College of Mining and Safety Engineering, Shandong University of Science and Technology, Qingdao, People's Republic of China, 3School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, People's Republic of China, 4School of Information Engineering, Guangdong University of Technology, Guangzhou, People's Republic of China, 5Department of Biomedical Engineering and Department of Electrical Engineering, The State University of New York, NY, United States

This paper develops a deep learning based multi-contrast MR imaging method. Unlike existing methods which mainly draw prior information from the target structure or a few reference images, we design a multi-contrast convolutional neural network to draw automatic feature descriptors for describing the multi-contrast correlations and identify the nonlinear mapping with the utilization of enormous existing multi-contrast MR images as training samples. Once the network is learned, it performs as a predicator for the online multi-contrast MR imaging. Experimental results on multi-contrast in vivo dataset show that the proposed method could restore lost information from the undersampled MR images while keeping their contrasts.

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