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Abstract #0642

1D Partial Fourier Parallel MR imaging with deep convolutional neural network

Shanshan Wang1, Ningbo Huang1,2, Tao Zhao1,3, Yong Yang2, Leslie Ying4, and Dong Liang1

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

This paper develops a multi-coil SuperCNN network for 1D Partial Fourier Parallel MR imaging. With the utilization of enormous existing undersampled multi-channel images as inputs and their corresponding square root of sum-of-squares of images obtained from the fully sampled data as labels, the network is trained to identify the nonlinear mapping relationship and then performed as a predicator to reconstruct the online MR images. Experimental results on an in vivo dataset show that the proposed multi-coil SuperCNN is able to reconstruct more accurate MR images in less time compared to GRAPPA and SPIRiT from the same amount of undersampled data.

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