Universal dynamic fitting of magnetic resonance spectroscopy

Abstract

PurposeDynamic (2D) MRS is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion‐weighted, relaxation‐weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modeling temporal changes in the parameters. Here, we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type.MethodsA simple user‐interface allows information to be shared and precisely modeled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting.ResultsAnalysis of synthetic functional 1H‐MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion‐weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data.ConclusionA toolbox for generalized and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open‐source software with comprehensive documentation, example data, and tutorials.

Model-based frequency-and-phase correction of1H MRS data with 2D linear-combination modeling

“…Finally, future work should also explore whether model-based FPC can be directly incorporated into more dynamic and complex 2D modeling scenarios like diffusion-weighted, functional, edited, hyperpolarized MRS, and relaxometry 45 . Recent studies of 2D modeling 21,45 have generally found improvements in fitting uncertainty and precision of target parameter estimation in several dynamic data scenarios. Certain benefits of 2D-LCM in uncertainty estimation were also shown for conventional 1D-MRS data 22 .…”

“…2D modeling can be biased by the incorrect selection of spectral models that do not describe data behavior correctly 21 -either in the spectral dimension or the second, indirect dimension 45 . It is wellknown that the choice of spectral model, modeling parameters, starting values, upper/lower bounds, regularizations, and penalties has an outsized impact on modeling results, even for 1D-LCM 30,[46][47][48][49][50][51] .…”

Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses

“…Alternative MRS analysis approaches involve 2D fitting, whereby multiple spectra are fit to a metabolite basis simultaneously - reducing the number of parameters requiring optimisation resulting in more accurate estimates. Whilst the advantages of 2D fitting in MRS have been long established in various contexts (Chong et al, 2011; Schulte & Boesiger, 2006; Van Ormondt et al, 1990) there has been renewed interest in the application of 2D fitting to functional and diffusion MRS (Clarke et al, 2024; Tal, 2023). Other approaches involve combining conventional 1D fitting with a GLM applied to the metabolite time-courses (Ligneul et al, 2021).…”

Investigating neurometabolite changes in response to median nerve stimulation

“…However, the Cramér–Rao lower bounds for Glu and GABA fitting were not significantly different across all scans. Recent dynamic fitting approaches for MRS, incorporating temporal modelling of the stimulation periods, may offer improved ability to detect changes compared with block-averaging (Clarke et al, 2024; Tal, 2023).…”