Physics-informed and uncertainty-aware deep learning approach for liver PDFF quantification

Motivation: Most Deep Learning (DL) methods to estimate liver PDFF require reference results for training and can only calculate deterministic outputs with unknown uncertainty. Goal(s): To estimate liver PDFF using a fully-unsupervised DL method for MR water-fat separation capable of quantifying uncertainty. Approach: We propose a physics informed DL-based framework which can be trained purely on chemical shift-encoded MR images. Our method estimates stochastic R2* and Δf maps, enabling uncertainty quantification, which are then used to obtain stochastic water-only and fat-only components. Results: Liver PDFF estimations showed good agreement with a reference technique, and uncertainty maps associated with imperfections in the considered physical model. Impact: The proposed physics-informed DL model requires only MR data for training, which facilitates the data gathering process. Moreover, our uncertainty-aware approach can quantify the uncertainty associated to the final estimations, which may be of significant value in clinical practice.