Influence of fitting approaches in LCModel on MRS quantification focusing on age-specific macromolecules and the spline baseline

Abstract

Quantification of neurochemical concentrations from ¹ H MR spectra is challenged by incomplete knowledge of contributing signals. Some experimental conditions hinder the acquisition of artifact-free spectra and impede the acquisition of condition-specific macromolecule (MM) spectra. This work studies differences caused by fitting solutions routinely employed to manage resonances from MM and lipids. High quality spectra (free of residual water and lipid artifacts and for which condition-specific MM spectra are available) are used to understand the influences of spline baseline flexibility and noncondition-specific MM on neurochemical quantification. Fitting with moderate spline flexibility or using noncondition-specific MM led to quantification that differed from when an appropriate, fully specified model was used. This occurred for all neurochemicals to an extent that varied in magnitude among and within approaches. The spline baseline was more tortuous when less constrained and when used in combination with noncondition-specific MM. Increasing baseline flexibility did not reproduce concentrations quantified under appropriate conditions when spectra were fitted using a MM spectrum measured from a mismatched cohort. Using the noncondition-specific MM spectrum led to quantification differences that were comparable in size with using a fitting model that had moderate freedom, and these influences were additive. Although goodness of fit was better with greater fitting flexibility, quantification differed from when fitting with a fully specified model that is appropriate for low noise data. Notable GABA and PE concentration differences occurred with lower estimates of measurement error when fitting with greater spline flexibility or noncondition-specific MM. These data support the need for improved metrics of goodness of fit. Attempting to correct for artifacts or absence of a condition-specific MM spectrum via increased spline flexibility and usage of noncondition-specific MM spectra cannot replace artifact-free data quantified with a condition-specific MM spectrum.

Keywords: 7 T; LCModel; macromolecules; magnetic resonance spectroscopy; ultrahigh field.

Figure 1.

Data and fitting quality. Representative ¹H MR spectrum from one older adult acquired from 8-mL VOI placed in the OCC (STEAM, 7 T, TR = 5 s, TE = 8 ms, number of averages = 64) fitted with five approaches summarized in Table 1 using LCModel. In vivo spectrum is shown together with LCModel fits (red lines), residuals (above), spline baseline contributions (below), and MM/lipid contributions (blue lines). Spectra are shown with no line-broadening applied.

Figure 2.

Spline baselines obtained with LCModel fitting. Average (black line) and standard deviation (shaded area) of individual spline baselines are shown for all 16 spectra fitted using five approaches summarized in Table 1. Spline baselines are shown with no line broadening.

Figure 3.

Concentrations of metabolites quantified using LCModel. Older adult spectra were fitted using five approaches summarized in Table 1. * - significant difference (p < 0.00089) between O-MM Stiff and other fitting approaches.