Keywords: Analysis/Processing, Fat and Fat/Water Separation
Motivation: Physics-guided learning has shown excellent performance in MRI field without the need for training data acquisition. The well-established mathematical model for fat-water separation allows the application of physics-guided learning.
Goal(s): Propose a pixel-by-pixel approach for fat-water separation with deep neural network trained with simulation data only.
Approach: Fat-water separation is performed using an artificial neural network trained on simulation data, with MR physics-based constraint. The network parameters are optimized to the target data for higher performance.
Results: The proposed network trained only on simulation data is applicable to in vivo data. The physics-guided learning and optimization provides stable training and improved image quality.
Impact: The proposed method allows to perform fat-water separation without the acquisition of training data yet successfully performs fat-water separation. Moreover, the pixelwise approach ensures fast training and inference with low computational cost.
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