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