Physics-informed Variational Auto-Encoder to generate synthetic multi-echo chemical shift-encoded liver MR images

Motivation: Deep Learning (DL)-based methods to quantify liver PDFF have had robustness difficulties due to the lack of large and heterogeneous training datasets with known results. Goal(s): To create a DL algorithm to synthesize realistic multi-echo liver MR images given a set of arbitrary MR scan parameters. Approach: To use a physics-driven approach to create a DL-based generative model able to synthesize realistic liver CSE-MR images with different compositions and geometries. Results: Our framework enabled a reliable customization of MR scan parameters, by directly adjusting them in the physical model. Feasibility of training a DL method purely based on synthetic data was also demonstrated. Impact: We successfully generated realistic multi-echo liver MR images with diverse geometries and compositions, which can be used to efficiently train DL-based methods for liver PDFF quantification. The physics-driven nature of our model enables the customization of MR scan parameters.