The Future is 2D: Spectral-Temporal Fitting of Dynamic Magnetic Resonance Spectroscopy Data Provides Exponential Gains in Precision Over Conventional Approaches

Abstract PurposeMany MRS paradigms produce 2D spectral-temporal datasets, including diffusion-weighted, functional, hyperpolarized and enriched (13C, 2H) 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. MethodsWe derived a Cramer Rao Lower Bound for the temporal parameters for both 1D and 2D approaches, for two experiments: A multi-echo (MTE) experiment, designed to estimate metabolite T2s; And a functional (fMRS) experiment, designed to estimate fractional change (δ) in metabolite concentrations. We investigated the dependence of the relative standard deviation of T2 in MTE and δ in fMRS. ResultsWhen 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. ConclusionOur results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as 1H and 2H, which exhibit substantial spectral overlap.