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 T ₂, T ₁, 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 T ₂s, and a functional MRS experiment designed to estimate fractional change ( $\delta$) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T ₂ in multi‐echo and $\delta$ 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.

Abstract

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