Model‐based frequency‐and‐phase correction of 1H MRS data with 2D linear‐combination modeling - Simicic, Dunja; Zöllner, Helge J.; Davies‐Jenkins, Christopher W.; Hupfeld, Kathleen E.; Edden, Richard A. E.; Oeltzschner, Georg (Magnetic resonance in medicine, 2024)
Purpose Retrospective frequency‐and‐phase correction (FPC) methods attempt to remove frequency‐and‐phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear‐combination model (2D‐LCM) of individual transients (“model‐based FPC”). We investigated how model‐based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D‐LCM in estimating frequency‐and‐phase drifts and, consequentially, metabolite level estimates. Methods We created synthetic in‐vivo‐like 64‐transient short‐TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D‐LCM with the traditional approach (spectral registration, averaging, then 1D‐LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground‐truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in‐vivo short‐TE PRESS data. Results 2D‐LCM estimates (and accounts for) frequency‐and‐phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D‐LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D‐LCM estimation of FPC and amplitudes performed substantially better at low‐to‐very‐low SNR. Conclusion Model‐based FPC with 2D linear‐combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low‐SNR conditions, for example, long TEs or strong diffusion weighting.