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Abstract #3529

Pixelwise Fat-Water Separation by Physics-guided Learning with Simulation Data

Seonghyuk Kim1 and Sung-Hong Park1,2
1Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, 2Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of

Synopsis

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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