The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches

Abstract

Purpose: Many MRS paradigms produce 2D spectral-temporal datasets, including diffusion-weighted, functional, and hyperpolarized and enriched (carbon-13, deuterium) experiments. Conventionally, temporal parameters—such as T2, T1, or diffusion constants—are assessed by first fitting each spectrum independently and subsequently fitting a temporal model (1D fitting). We investigated whether simultaneously fitting the entire dataset using a single spectral-temporal model (2D fitting) would improve the precision of the relevant temporal parameter. Methods: We derived a Cramer Rao lower bound for the temporal parameters for both 1D and 2D approaches for 2 experiments: a multi-echo experiment designed to estimate metabolite T2s, and a functional MRS experiment designed to estimate fractional change ((Formula presented.)) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T2 in multi-echo and (Formula presented.) in functional MRS. Results: When peaks were spectrally distant, 2D fitting improved precision by approximately 20% relative to 1D fitting, regardless of the experiment and other parameter values. These gains increased exponentially as peaks drew closer. Dependence on temporal model parameters was weak to negligible. Conclusion: Our results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as hydrogen and deuterium, which exhibit substantial spectral overlap.