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Abstract #3215

Ultrafast water-fat separation using deep learning-based single-shot MRI

Xinran Chen1, Wei Wang1, Jianpan Huang2, Jian Wu1, Lin Chen1, Congbo Cai1, Shuhui Cai1, and Zhong Chen1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China


Water-fat separation is a powerful tool in diagnosing many diseases and many efforts have been made to reduce the scan time. Spatiotemporally encoded (SPEN) single-shot MRI, as an emerging ultrafast MRI method, can accomplish the fastest water-fat separation since only one shot is required. However, the SPEN water/fat images obtained by the state-of-the art methods still have some shortcomings. Here, a deep learning approach based on U-Net was proposed to obtain SPEN water/fat images simultaneously with improved spatial resolution, better fidelity and reduced reconstruction time. The efficiency of our method is demonstrated by numerical simulations, and in vivo rat experiments.

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