Keywords: Other AI/ML, Machine Learning/Artificial Intelligence, Generative Model
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.
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