Improved Fat-Water separation with Deep Learning-based ad-hoc MRI reconstruction incorporating spatial smoothing.

Motivation: The novel Deep Learning (DL)-based Ad-Hoc Reconstruction (AHR) method for fat-water separation in Multi Echo-Magnetic Resonance Imaging (ME-MRI) has absolute generalizability. It can perform fat-water separation with the ME-MRIs from any anatomical region and views with varied numbers of echoes. Goal(s): This research investigates the fat-water separation performance of spatial smoothing incorporated DL-based AHR method in ME-MRIs with and without noise. Approach: The fat-water separation biophysical model based loss in AHR is added with spatial smoothing constraints. Results: Results demonstrate that incorporating spatial smoothing in AHR improves the fat-water separation performance in ME-MRIs without noise, however, no performance improvements in ME-MRIs containing noise. Impact: The PDFF maps obtained from fat-water separation in Multi Echo-MRI (ME-MRI) are of diagnostic and prognostic value in many diseases. This study investigates the performance of a novel Deep Learning-based Ad-Hoc Reconstruction method with spatial smoothing for fat-water separation.