Abstract #3985

Feasibility of Multi-contrast MR imaging via deep learning

Shanshan Wang¹, Tao Zhao^1,2, Ningbo Huang^1,3, Sha Tan^1,4, 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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