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Abstract #3860

Accelerated water-fat separation based on deep learning model exploring multi-echo nature of mGRE

Qinjia Bao1, Xiaojun Li2, Kewen Liu2, Zhao Li3, Hongxia Xiong2, Jingjie Yan4, Yalei Chen2, and Chaoyang Liu3
1Weizmann Institute of Science, Rehovot, Israel, 2School of Information Engineering, Wuhan University of Technology, Wuhan, China, 3State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China, 4Huazhong University of Science and Technology, Wuhan, China

We proposed a novel deep learning network for water-fat separation from undersampled mGRE data. The network contains three components: The first is the reconstruction module, which can effectively take advantage of the similarity between different echoes to recover the fully sampled image from the undersampled data; the second is the feature extraction module, which learns the correlations between consecutive echoes; and the third is the water-fat separation module that processes the feature information extracted from the feature extraction module. The results show that the proposed network can effectively obtain high-quality water and fat images at 6 times acceleration.

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