Robust Retrospective Frequency and Phase Correction for Single-voxel MR Spectroscopy

TL;DR

This paper introduces RATS, a robust retrospective correction method for single-voxel MR spectroscopy that effectively handles large frequency shifts and baseline instability, improving data quality and analysis accuracy.

Contribution

The novel RATS method incorporates variable-projection and baseline fitting to enhance robustness against baseline fluctuations and large frequency shifts in MRS data.

Findings

RATS improves accuracy and stability over existing methods.

Reduced artifacts in glutathione-edited MRS data.

Effective correction in the presence of large frequency shifts.

Abstract

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.