Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing

Abstract

Objective: To accelerate tissue sodium concentration (TSC) quantification of skeletal muscle using 23Na MRI and 3D dictionary-learning compressed sensing (3D-DLCS). Materials and methods: Simulations and in vivo 23Na MRI examinations of calf muscle were performed with a nominal spatial resolution of Δx=(3.0×3.0×15.0)mm3. Fully sampled and three undersampled 23Na MRI data sets (undersampling factors (USF) = 3, 4.4, 6.7) were evaluated. Ten healthy subjects were examined on a 3 Tesla MRI system. Results of the simulation study and the in vivo measurements were compared to the ground truth (GT) and the fully sampled fast Fourier transform (NUFFT) reconstruction, respectively. Results: Reconstruction results of simulated data with optimized 3D-DLCS yielded a lower deviation (< 4%) from the GT than results of the NUFFT reconstruction (> 5%) and a lower standard deviation (SD). For in vivo measurements, a TSC of 17±2.7mMol/l was observed. The mean deviation from the reference is lower for the undersampled 3D-DLCS reconstructions (3.4%) than for NUFFT reconstructions (4.6%). SD is reduced using 3D-DLCS. Compared to a fully sampled NUFFT reconstruction, acquisition time could be reduced by a factor of 4.4 while maintaining similar quantitative accuracy. Discussion: The optimized 3D-DLCS reconstruction enables accelerated TSC measurements with high quantification accuracy.