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Abstract #3226

Semi-Supervised Image Domain Transfer for Dixon Water and Fat Separation

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

Deep learning neural-networks for Dixon imaging require a large number of “paired” input and output images for network training. Moreover, the previous methods require Dixon images as their network input, thus they could not be used to reconstruct water images from regular T1 or T2-weighted images. In this work, we propose an image domain transfer based deep-learning network which can reconstruct water images from either T1 or T2-weighted MR images. Using semi-supervised learning, two separate groups of “unpaired and unordered” input and output images were used to translate either T1 or T2-weighted images to their corresponding water-only images.

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