Fast water/fat T 2 and PDFF mapping via multiple overlapping-echo detachment acquisition and deep learning reconstruction

Abstract 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 water T 2 values to assess muscle damage and proton density fat fraction (PDFF) to quantify fat infiltration. However, conventional methods for simultaneous water-fat separation and T 2 quantification often require long acquisition times. This study aims to develop an ultrafast method for simultaneous water-fat separation and T 2 quantification. 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 and in vivo scans, were performed on a 3T MRI scanner. The in vivo experiments 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, water T 2 , and fat T 2 maps. Main results. In numerical experiments ( T 2 range: water 19–160 ms, fat 15–160 ms; PDFF range: 14%–100%), the R 2 values were all 0.999 for water T 2 , fat T 2 , and PDFF, with average percentage errors of 3.16%, 0.98%, and 0.78%, respectively. In phantom experiments ( T 2 range: water 35–125 ms, fat 36–47 ms; PDFF range: 14–36%), the R 2 values were 0.995, 0.733, and 0.996 for water T 2 , fat T 2 , 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 and in vivo experiments. In patient scans, CSE-MOLED successfully distinguished between fat infiltration and muscle damage. Significance . CSE-MOLED simultaneously obtains T 2 and proton density maps for both water and fat, along with T 2 -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.