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arXiv · 2004.07923

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

R. Jafari·P. Spincemaille·J. Zhang·T. D. Nguyen·M. R. Prince·X. Luo·J. Cho·D. Margolis·Y. Wang
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Abstract

Purpose: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. Methods: The current T2*-IDEAL algorithm for solving fat/water separation is dependent on initialization. Recently, deep neural networks (DNN) have been proposed to solve fat/water separation without the need for suitable initialization. However, this approach requires supervised training of DNN (STD) using the reference fat/water separation images. Here we propose two 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 (NTD) using physical cost and backpropagation to directly reconstruct a single dataset. The STD, UTD and NTD methods were compared with the reference T2*-IDEAL. Results: All DNN methods generated consistent water/fat separation results that agreed well with T2*-IDEAL under proper initialization. Conclusion: The water/fat separation problem can be solved using unsupervised deep neural networks.

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R. Jafari, P. Spincemaille, J. Zhang, T. D. Nguyen, M. R. Prince, X. Luo, J. Cho, D. Margolis, Y. Wang. 2020-04-16. Deep Neural Network (DNN) for Water/Fat Separation: Supervised Training, Unsupervised Training, and No Training. https://arxiv.org/abs/2004.07923

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