Robust retrospective frequency and phase correction for single‐voxel MR spectroscopy

• Published in: Magnetic resonance in medicine Vol. 81; no. 5; pp. 2878 - 2886
• Main Author: Wilson, Martin
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.05.2019
• Subjects: Algorithms; Brain - diagnostic imaging; Computer Simulation; drift; Frequency stability; Glutathione; Glutathione - chemistry; Healthy Volunteers; Humans; Image Processing, Computer-Assisted - methods; Lipids - chemistry; Magnetic Resonance Spectroscopy; Motion; MRS; Optimization; Quality assessment; Quality control; RATS; Signal-To-Noise Ratio; Spectroscopy; Spectrum analysis; Subtraction; SVS; Variation; VARPRO; Water; RATS; SVS; motion; VARPRO; MRS; drift
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.27605

Purpose Subject motion and static field (B0) 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 (>7 Hz) 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. Conclusions 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.