Retrospective frequency drift correction of rosette MRSI data using spectral registration

Abstract

Purpose Frequency drift correction is an important postprocessing step in MRS that yields improvements in spectral quality and metabolite quantification. Although routinely applied in single‐voxel MRS, drift correction is much more challenging in MRSI due to the presence of phase‐encoding gradients. Thus, separately acquired navigator scans are normally required for drift estimation. In this work, we demonstrate the use of self‐navigating rosette MRSI trajectories combined with time‐domain spectral registration to enable retrospective frequency drift corrections without the need for separately acquired navigator echoes. Methods A rosette MRSI sequence was implemented to acquire data from the brains of 5 healthy volunteers. FIDs from the center of k‐space (k=0$$ k=0 $$ FIDs) were isolated from each shot of the rosette acquisition, and time‐domain spectral registration was used to estimate the frequency offset of each k=0$$ k=0 $$ FID relative to a reference scan (the first k=0$$ k=0 $$ FID in the series). The estimated frequency offsets were then used to apply corrections throughout k$$ k $$‐space. Improvements in spectral quality were assessed before and after drift correction. Results Spectral registration resulted in significant improvements in signal‐to‐noise ratio (12.9%) and spectral linewidths (18.5%). Metabolite quantification was performed using LCModel, and the average Cramer‐Rao lower bounds uncertainty estimates were reduced by 5.0% for all metabolites, following field drift correction. Conclusion This study demonstrated the use of self‐navigating rosette MRSI trajectories to retrospectively correct frequency drift errors in in vivo MRSI data. This correction yields meaningful improvements in spectral quality.

Single‐shot diffusion trace spectroscopic imaging using radial echo planar trajectories

“…To account for the different k-space acquisition times throughout the readout, a linear frequency-dependent phase correction was applied to the even and odd k-space data. 50,51 Subsequently, the even echo data were time-shifted by half the spectral dwell time before averaging with the odd echo data [52][53][54] The resulting time signal was further pre-processed using navigator FIDs 26,55,56 before final reconstruction, as described in the Supporting Information.…”