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    To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. The current T 2 ∗ -IDEAL algorithm for solving water/fat separation is dependent on initialization. Recently, DNN has been proposed to solve water/fat separation without the need for suitable initialization. However, this approach requires supervised training of DNN using the reference water/fat separation images. Here we propose 2 novel DNN water/fat separation methods: 1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no training of DNN using physical cost and backpropagation to directly reconstruct a single dataset. The supervised training of DNN, unsupervised training of DNN, and no training of DNN methods were compared with the reference T 2 ∗ -IDEAL. All DNN methods generated consistent water/fat separation results that agreed well with T 2 ∗ -IDEAL under proper initialization. The water/fat separation problem can be solved using unsupervised deep neural networks. © 2020 International Society for Magnetic Resonance in Medicine.


    Ramin Jafari, Pascal Spincemaille, Jinwei Zhang, Thanh D Nguyen, Xianfu Luo, Junghun Cho, Daniel Margolis, Martin R Prince, Yi Wang. Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training. Magnetic resonance in medicine. 2021 Apr;85(4):2263-2277

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    PMID: 33107127

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