High-variability synthetic fat-water MRI dataset for testing the robustness of Deep Learning-based reconstruction models

Motivation: Deep Learning (DL) models have recently been used for fat-water separation in Multi-Echo MRI (ME-MRI). However, DL models may not always be robust and under-perform when not trained with a large and diverse dataset. Goal(s): This research proposes high-variability synthetic ME-MRI generated using the biophysical model of fat-water separation as a tool for testing the generalizability and robustness of DL-based fat-water separation models. Approach: High-variability synthetic ME-MRI was used to evaluate the robustness of the recent state-of-the-art DL-based Ad-Hoc Reconstruction (AHR) method for fat-water separation. Results: The AHR method lacked robustness and synthetic ME-MRIs can be effectively used to test DL models. Impact: The fat-water maps obtained by processing the Multi Echo-MRI (ME-MRI) are of diagnostic and prognostic value in many diseases. This study investigates the role of synthetic ME-MRIs with high variability in testing the robustness of Deep Learning-based fat-water separation models.