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 (δ$$ \delta $$) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T2 in multi‐echo and δ$$ \delta $$ 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.

Diffusion‐weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

“…Indeed, a recent study on a set of synthetic dMRS spectra found a more reliable estimate of diffusion and tissue properties when simultaneous fitting is applied. 92 This is supported by Tal et al, 93 showing that simultaneous 2D fitting improved precision by at least 20% compared to 1D fitting.…”

Universal dynamic fitting of magnetic resonance spectroscopy

“…Recently the advantages of dynamic fitting of 2D data (also called spectral-temporal fitting) were demonstrated theoretically and numerically. 34 Here, we replicated these results using the software framework of FSL-MRS and extended the simulations from toy (two resonance) examples to realistic 1 H-fMRS data, containing many overlapping spectral resonances. Functional MRS temporally resolves MRS to detect changes in neurochemical concentrations (or metabolite visibility), induced by external sensory stimulus or otherwise evoked neural activity.…”

GABA, glutamatergic dynamics and BOLD contrast assessed concurrently using functional MRS during a cognitive task

“…While the present analysis makes no particular assumptions regarding the shape of the GRF (beyond the restricted T S‐A range), we note that recent studies have begun to investigate putative metabolite response functions 99 ; once an appropriate response function is determined, incorporation of this into the present model (in place of the coarse binning) will be trivially accomplished, and may well improve sensitivity to subtle variations. Furthermore, while the present model reconstructs discrete sub‐spectra which are fitted independently using existing 1D modelling tools, we note recent developments towards simultaneous, 2D spectral‐temporal fitting 100 of multiple spectra linked by an arbitrary model, such as that implemented in the FSL‐MRS dynamic fitting module 22,101 . With appropriate constraints, incorporating our linear model into such an approach may further improve fitting performance, particularly for lower‐SNR cases.…”

“…Functional events were binned according to the achieved interval between stimulus and acquisition (T S-A ). Five bins (bin 1-5 ) were defined, with edges at [100,183,267,350] ms, open at either end, lower limits inclusive; the inner three bins evenly cover the nominated 100-350 ms T S-A range. This resulted in approximately 48 task-ON metabolite transients per bin (with some individual variation), and 320 task-OFF metabolite transients.…”