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Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training
Ramin Jafari1,2, Pascal Spincemaille2, Jinwei Zhang1,2
1Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
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 -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 -IDEAL.
Results:
All DNN methods generated consistent water/fat separation results that agreed well with -IDEAL under proper initialization.
Conclusion:
The water/fat separation problem can be solved using unsupervised deep neural networks.
