Keywords: AI/ML Image Reconstruction, Quantitative Imaging, Fat-water seperation, PDFF, Deep Learning, Fat Quantification, Physics Informed Deep Learning, Synthetic MRI
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.
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