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Abstract #0823

Water and Fat Separation with a Dixon Conditional Generative Adversarial Network (DixonCGAN)

Jong Bum Son1, Ken-Pin Hwang1, Marion E. Scoggins2, Basak E. Dogan3, Gaiane M. Rauch2, Mark D. Pagel4, and Jingfei Ma1
1Imaging Physics Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 2Diagnostic Radiology Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 3Department of Diagnostic Radiology, The University of Texas Southwestern Medical Center, Dallas, TX, United States, 4Cancer Systems Imaging Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

A Dixon conditional generative adversarial network (DixonCGAN) was developed for Dixon water and fat separation. For the robust water image reconstruction, DixonCGAN performs water and fat separation with three processing steps: (1) phase-correction with DixonCGAN, (2) error-correction for DixonCGAN processing, and (3) the final water and fat separation. A conditional generative adversarial network (CGAN) originally designed to change photo styles could be successfully modified to perform phase-correction with improved global and local image details. Moreover, localized deep-learning processing errors could be effectively recovered with the proposed deep-learning error-correction processes.

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