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Abstract #2781

Complex-valued residual network learning for parallel MR imaging

Shanshan Wang1, Huitao Cheng1, Ziwen Ke1, Leslie Ying2, Xin Liu1, Hairong Zheng1, and Dong Liang1

1Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen, China, 2Department of Biomedical Engineering and Department of Electrical Engineering, The State University of New York, Buffalo, NY, United States

Applying deep learning to fast MR imaging has been new and highly evolved. This direction utilizes networks to draw valuable prior information from available big datasets and then assists fast online imaging. Nevertheless, most existing works adopt real-valued network structures while MR images are complex-valued. This paper proposes a complex-valued residual network learning framework for parallel MR imaging. Specifically, complex-valued convolution and initialization strategy are provided. Residual connections are also adopted to learn a more accurate prior. Experimental results show that the proposed method could achieve improved complex-valued image reconstruction with much less time compared to GRAPPA and SPIRiT.

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