Abstract #3984

Undersampling trajectory design for fast MRI with super-resolution convolutional neural network

Shanshan Wang¹, Taohui Xiao^1,2, Sha Tan^1,3, Yuanyuan Liu¹, 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 Physics and Optoelectronics, Xiangtan University, Xiangtan, People's Republic of China, ³School of Information Engineering, Guangdong University of Technology, Guangzhou, People's Republic of China, ⁴Department of Biomedical Engineering and Department of Electrical Engineering, The State University of New York, NY, United States

Deep learning based fast MR imaging (DeepLearnMRI) has been an appealing new research direction, which utilizes networks to draw valuable prior information from enormous existing high-quality MR images and then assists accurate MR image reconstruction from undersampled data. This paper explores optimal undersampling trajectory for DeepLearnMRI. Specifically, we designed hamming filtered asymmetrical 1D partial Fourier sampling scheme for fast MR imaging with our developed super-resolution convolutional neural network. Experimental results on in vivo dataset show that the proposed scheme allows DeepLearnMRI to reconstruct more accurate MR images with less time compared to the Classical GRAPPA and SPIRiT.

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