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.