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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