Fast water/fat T2 and PDFF mapping via multiple overlapping‑echo detachment acquisition and deep learning reconstruction

Abstract

Rapid and accurate quantitative assessment of muscle tissue characteristics is critical for the diagnosis and monitoring of neuromuscular diseases (NMDs). Quantitative magnetic resonance imaging enables non-invasive assessment of muscle pathology by using water T2 values to detect muscle damage and proton density fat fraction (PDFF) to quantify fat infiltration. However, conventional methods for simultaneous water-fat separation and T2 quantification often require long acquisition times. This study aims to develop an ultrafast method for simultaneous water-fat separation and T2 quantification.&#xD;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. The proposed method was validated through numerical simulations, phantom studies, and in vivo experiments involving five healthy volunteers, one subject with muscle atrophy, and one with muscle damage.&#xD;Main results: In numerical experiments, the R2 values were all 0.999 for water T2, fat T2, and PDFF. In phantom experiments, the R2 values were 0.995, 0.733, and 0.996 for water T2, fat T2, and PDFF, 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.&#xD;Significance: CSE-MOLED simultaneously obtains T2 and proton density maps for both water and fat, along with T2-corrected PDFF map, in 162 ms per slice, offering the potential to enhance the diagnostic accuracy of NMDs without increasing the clinical scanning burden.

Keywords: MOLED; PDFF; T2 quantification; deep learning; neuromuscular diseases.