Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training

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 $T_2^{\ast }$ -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 $T_2^{\ast }$ -IDEAL.

Results: All DNN methods generated consistent water/fat separation results that agreed well with $T_2^{\ast }$ -IDEAL under proper initialization.

Conclusion: The water/fat separation problem can be solved using unsupervised deep neural networks.

Keywords: deep learning; label free; unsupervised; water/fat separation.