Fast water/fatT₂and PDFF mapping via multiple overlapping-echo detachment acquisition and deep learning...

Objective.Rapid and accurate quantitative assessment of muscle tissue characteristics is valuable for the diagnosis and monitoring of neuromuscular diseases (NMDs). Quantitative magnetic resonance imaging (MRI) enables non-invasive assessment of muscle pathology by using waterT₂values to assess muscle damage and proton density fat fraction (PDFF) to quantify fat infiltration. However, conventional methods for simultaneous water-fat separation andT₂quantification often require long acquisition times. This study aims to develop an ultrafast method for simultaneous water-fat separation andT₂quantification.Approach.A novel water-fat separation framework that combines chemical shift encoding with the multiple overlapping-echo detachment sequence (CSE-MOLED) was proposed. Synthetic training data and deep learning-based reconstruction were employed to address challenges in water-fat separation, including the complex multi-peak spectral characteristic of fat and the non-idealities in MRI acquisition. All experiments, including phantom andin vivoscans, were performed on a 3T MRI scanner. Thein vivoexperiments focused on the human thigh and involved five healthy volunteers, one subject with muscle atrophy, and one with muscle damage. The CSE-MOLED sequence was acquired with a spatial resolution of 1.72 mm × 1.72 mm × 5 mm. For reference, mDixon-TSE (Turbo Spin Echo) was performed to generate PDFF, waterT₂, and fatT₂maps.Main results.In numerical experiments (T₂range: water 19-160 ms, fat 15-160 ms; PDFF range: 14%-100%), theR²values were all 0.999 for waterT₂, fatT₂, and PDFF, with average percentage errors of 3.16%, 0.98%, and 0.78%, respectively. In phantom experiments (T₂range: water 35-125 ms, fat 36-47 ms; PDFF range: 14-36%), theR²values were 0.995, 0.733, and 0.996 for waterT₂, fatT₂, and PDFF, with average percentage errors of 5.56%, 2.93%, and 1.77%, respectively. High repeatability (coefficient of variation <2.0%) was achieved in both phantom andin vivoexperiments. In patient scans, CSE-MOLED successfully distinguished between fat infiltration and muscle damage.Significance. CSE-MOLED simultaneously obtainsT₂and proton density maps for both water and fat, along withT₂-corrected PDFF map, in 162 ms per slice of acquisition time, offering the potential to enhance the diagnostic accuracy of NMDs without increasing the clinical scanning burden.