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Abstract #0680

Construction of water-fat separation deep learning model combined with multi-echo nature of gradient-recalled echo sequence

Kewen Liu1, Xiaojun Li1, Qinjia Bao2, Chaoyang Liu3, Hongxia Xiong4, Zhao Li3, Yuan Ma1, Panpan Fang1, and Yalei Chen1
1School of Information Engineering, Wuhan University of Technology, Wuhan, China, 2United Imaging of Scientific Instruments, Shanghai, China, 3State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathmatics, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China, 4School of Civil Engineering & Architecture, Wuhan University of Technology, Wuhan, China

We proposed a novel deep learning network architecture (MEBC-RCAN) for water-fat separation based on multi-echo GRE sequence. The network architecture contains three main components: the first part is Multi-Echo Bidirectional Convolutional (MEBC) to explore the correlations of successive images in multi-echo GRE; the second part is Residual Channel Attention (RCA) network to mimic the iterative optimization in traditional water-fat separation method; and the third part is Multi-Layer Feature Fusion (MLFF) to combine separation information learned from every RCA network. The results show that the proposed network could effectively obtain the high-quality water and fat images from clinical multi-echo GRE data.

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