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

Ramin Jafari^1,2, Pascal Spincemaille², Jinwei Zhang^1,2

¹Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.

Magnetic Resonance in Medicine

|October 27, 2020

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Unsupervised deep neural networks (DNNs) offer a novel solution for water/fat separation, eliminating the need for initialization. This study demonstrates that DNNs, particularly unsupervised methods, can achieve accurate results comparable to traditional algorithms.

Area of Science:

Medical Imaging
Computational Science

Background:

Water/fat separation is a critical step in various imaging techniques.
Current algorithms like IDEAL require specific initialization, which can be a limitation.
Deep Neural Networks (DNNs) have emerged as a potential solution for automated water/fat separation.

Purpose of the Study:

To investigate the application of DNNs for optimizing water/fat separation.
To compare the efficacy of supervised and unsupervised DNN training methods for this task.

Main Methods:

Proposed two novel DNN-based water/fat separation methods: unsupervised training of DNN (UTD) and DNN with no training.
UTD utilized the physical forward problem as its cost function.
The no-training DNN method employed physical cost and backpropagation for direct reconstruction.

Keywords:

deep learning label free unsupervised water/fat separation

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Main Results:

All DNN approaches, including supervised, unsupervised, and no-training methods, produced water/fat separation results consistent with the IDEAL algorithm.
The agreement was particularly strong when IDEAL was used with appropriate initialization.

Conclusions:

Unsupervised deep neural networks provide a viable and effective approach to solving the water/fat separation problem.
DNN methods offer a promising alternative to traditional algorithms, potentially reducing reliance on manual initialization.