Free-breathing high isotropic resolution quantitative susceptibility mapping (QSM) of liver using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction

Abstract

Purpose: To enable free-breathing and high isotropic resolution liver quantitative susceptibility mapping (QSM) using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction. Methods: Using 3D multi-echo UTE cones MRI, a respiratory motion was estimated from the k-space center of the imaging data. After sorting the k-space data with estimated motion, respiratory motion state-resolved reconstruction was performed for multi-echo data followed by nonlinear least-squares fitting for proton density fat fraction (PDFF), R-2*, and fat-corrected B-0 field maps. PDFF and B-0 field maps were subsequently used for QSM reconstruction. The proposed method was compared with motion-averaged (gridding) reconstruction and conventional 3D multi-echo Cartesian MRI in moving gadolinium phantom and in vivo studies. Region of interest (ROI)-based linear regression analysis was performed on these methods to investigate correlations between gadolinium concentration and QSM in the phantom study and between R-2* and QSM in in vivo study. Results: Cones with motion-resolved reconstruction showed sharper image quality compared to motion-averaged reconstruction with a substantial reduction of motion artifacts in both moving phantom and in vivo studies. For ROI-based linear regression analysis of the phantom study, susceptibility values from cones with motion-resolved reconstruction (QSM(ppm) = 0.31 x gadolinium(mM)+ 0.05, R-2 = 0.999) and Cartesian without motion (QSM(ppm) = 0.32 x gadolinium(mM)+ 0.04, R-2 = 1.000) showed linear relationships with gadolinium concentrations and showed good agreement with each other. For in vivo, motion-resolved reconstruction showed higher goodness of fit (QSM(ppm) = 0.00261 x R-2*(s-1) - 0.524, R-2 = 0.977) compared to motion-averaged reconstruction (QSM(ppm) = 0.0021 x R-2*(s-1) - 0.572, R-2= 0.723) in ROI-based linear regression analysis between R-2* and QSM.