Dixon-based B0 self-navigation in radial stack-of-stars multi-echo gradient echo imaging.

Abstract

To develop a Dixon-based self-navigation approach to estimate and correct temporal variations in radial stack-of-stars gradient echo imaging for quantitative body MRI. The proposed method estimates temporal variations using a self-navigator estimated by a graph-cut-based water-fat separation algorithm on the oversampled k-space center. The self-navigator was employed to correct for phase differences between radial spokes (one-dimensional [1D] correction) and to perform a motion-resolved reconstruction to correct spatiotemporal pseudo-periodic variations (three-dimensional [3D] correction). Numerical simulations, phantom experiments and in vivo neck scans were performed to evaluate the effects of temporal variations on the field-map, proton density fat fraction (PDFF) and map, and to validate the proposed method. Temporal variations were found to cause signal loss and phase shifts on the multi-echo images that lead to an underestimation of , while PDFF mapping was less affected. The self-navigator captured slowly varying temporal drifts and temporal variations caused by respiratory motion. While the 1D correction effectively corrected drifts in phantom studies, it was insufficient in vivo due to 3D spatially varying temporal variations with amplitudes of up to 25 Hz at 3 T near the lungs. The proposed 3D correction locally improved the correction of field-map and and reduced image artifacts. Temporal variations particularly affect mapping in radial stack-of-stars imaging. The self-navigation approach can be applied without modifying the MR acquisition to correct for drift and physiological motion-induced variations, especially in the presence of fat.