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Purpose: To use a deep neural network (DNN) for solving the optimizationproblem of water/fat separation and to compare supervised and unsupervisedtraining.  Methods: The current T2*-IDEAL algorithm for solving fat/water separation isdependent on initialization. Recently, deep neural networks (DNN) have beenproposed to solve fat/water separation without the need for suitableinitialization. However, this approach requires supervised training of DNN(STD) using the reference fat/water separation images. Here we propose twonovel 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 (NTD) using physical cost and backpropagation to directlyreconstruct a single dataset. The STD, UTD and NTD methods were compared withthe reference T2*-IDEAL.  Results: All DNN methods generated consistent water/fat separation resultsthat agreed well with T2*-IDEAL under proper initialization.  Conclusion: The water/fat separation problem can be solved using unsuperviseddeep neural networks.

Deep Neural Network (DNN) for Water/Fat Separation: Supervised Training, Unsupervised Training, and No Training