Abstract #0642

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

Shanshan Wang¹, Ningbo Huang^1,2, Tao Zhao^1,3, 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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