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

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