  Purpose: subject motion and static field (B$_0$) drift are known to reduce
the quality of single voxel MR spectroscopy data due to incoherent averaging.
Retrospective correction has previously been shown to improve data quality by
adjusting the phase and frequency offset of each average to match a reference
spectrum. In this work, a new method (RATS) is developed to be tolerant to
large frequency shifts (greater than 7Hz) and baseline instability resulting
from inconsistent water suppression. Methods: in contrast to previous
approaches, the variable-projection method and baseline fitting is incorporated
into the correction procedure to improve robustness to fluctuating baseline
signals and optimization instability. RATS is compared to an alternative
method, based on time-domain spectral registration (TDSR), using simulated data
to model frequency, phase and baseline instability. In addition, a J-difference
edited glutathione in-vivo dataset is processed using both approaches and
compared. Results: RATS offers improved accuracy and stability for large
frequency shifts and unstable baselines. Reduced subtraction artifacts are
demonstrated for glutathione edited MRS when using RATS, compared with
uncorrected or TDSR corrected spectra. Conclusion: the RATS algorithm has been
shown to provide accurate retrospective correction of SVS MRS data in the
presence of large frequency shifts and baseline instability. The method is
rapid, generic and therefore readily incorporated into MRS processing pipelines
to improve lineshape, SNR and aid quality assessment.
