Chemical shift and relaxation regularisation improve the accuracy of ¹ H MR spectroscopy analysis

Purpose Accurate analysis of metabolite levels from 1 H MRS data is a significant challenge, typically requiring the estimation of approximately 100 parameters from a single spectrum. Signal overlap, spectral noise and common artefacts further complicate analysis, leading to instability and reports of poor agreement between different analysis approaches. One inconsistently used method to improve analysis stability is known as regularisation, where poorly determined parameters are partially constrained to take a predefined value. In this study we examine how regularisation of frequency and linewidth parameters influences analysis accuracy. Methods The accuracy of three MRS analysis methods was compaired: 1) ABfit, 2) ABfit-reg and 3) LCModel, where ABfit-reg is a modified version of ABfit incorporating regularisation. Accuracy was assessed on synthetic MRS data generated with random variability in the frequency shift and linewidth parameters applied to each basis signal. Spectra (N=1000) were generated across a range of SNR values (10, 30, 60, 100) to evaluate the impact of variable data quality. Results Comparison between ABfit and ABfit-reg demonstrates a statistically significant ( p < 0.0005) improvement in accuracy associated with regularisation for each SNR regime. An approximately 10% reduction in the mean squared metabolite errors were found for ABfit-reg compared to LCModel for SNR > 10 ( p < 0.0005). Furthermore, Bland-Altman analysis shows that incorporating regularisation into ABfit enhances its agreement with LCModel. Conclusion Regularisation is beneficial for MRS fitting and accurate characterisation of the frequency and linewidth variability in vivo may yield further improvements.