Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation

Motivation: Modeling and application of dynamic MRS is receiving growing interest in the community. Goal(s): Accuracy, precision, and uncertainty of 2D modeling algorithms must be carefully characterized. Approach: Here, we generated synthetic spectra of an idealized (single metabolite with a stable signal across transients) conventional 1D-MRS experiment. We then compared accuracy, precision, and uncertainty estimation between a 2D model of all transients without averaging and a 1D model of the averaged spectrum. Results: Both models performed similarly in terms of accuracy and precision. 2D-LCM afforded small benefits for uncertainty estimation for uncorrelated noise and substantial benefits for correlated noise. Impact: For conventional (non-dynamic, multi-transient) MRS data, 2D-LCM without averaging and 1D-LCM after averaging perform similarly accurate and precise. 2D-LCM affords gains in uncertainty estimation that appear to be related to the degree of noise correlation across transients.