Investigation of the influence of macromolecules and spline baseline in the fitting model of human brain spectra at 9.4T

Abstract

Purpose In this study, the influence of experimentally measured macromolecules and spline baseline on the quantification results of proton MRS data was investigated. Methods Proton MRS spectra from the left parietal lobe and the occipital lobe were acquired at 9.4T in the human brain using metabolite‐cycled semi‐LASER. Then, the left parietal lobe data, along with the occipital lobe, spectra were quantified and the influence of the inclusion of experimentally measured macromolecular basis sets in the fitting model was evaluated. Furthermore, the effect of the stiffness of the fitted spline baselines on the resulting metabolite concentrations was evaluated. Results In general, concentrations were higher for metabolites in occipital lobe than the left parietal lobe. The inclusion of an experimentally acquired measured macromolecular basis set from another brain region neither affected the quantification results nor the resulting spline baselines significantly. A highly flexible spline baseline led to overestimation or underestimation of metabolite concentrations. Differences of above 15% in the quantification of metabolite levels for both lobes were observed for several metabolites using LCModel default settings for spline baselines and macromolecules in comparison to stiffer spline baselines. Conclusion Fitting with the default LCModel macromolecular basis set and spline baseline model had significant influence in the resulting spline baselines, leading to large deviations both in the concentrations and fitted macromolecular components. The number of knots in the spline may create overflexible baselines, which can potentially lead to quantification errors. Interestingly, the interchange of macromolecular basis set between occipital lobe and left parietal lobe spectra had less influence on the quantification results compared to the default LCModel settings.

T2 relaxation times of macromolecules and metabolites in the human brain at 9.4 T

“…The metabolite spectra summed across subjects were fitted similarly. Because the summed MMB spectra were included in the fit, DKNTMN was set to 0.25, resulting in a stiffer LCModel baseline compared with the default LCModel value of 0.15 …”

Test–Retest Reliability of the Brain Metabolites GABA and Glx With JPRESS, PRESS, and MEGA‐PRESS MRS Sequences in vivo at 3T

“…In the case of PRESS, a double inversion recovery spectrum was acquired to measure the macromolecular (MM) experimental baseline. The MM baseline was included in the basis set for PRESS analyses, since there is substantial contribution of macromolecules at the applied TE = 32 msec . The JPRESS spectroscopic data were analyzed by using ProFit2 (Prior‐knowledge Fitting) including 18 metabolites .…”

“…The MM baseline was included in the basis set for PRESS analyses, since there is substantial contribution of macromolecules at the applied TE = 32 msec. 31,32 The JPRESS spectroscopic data were analyzed by using ProFit2 (Prior-knowledge Fitting) including 18 metabolites. 9 The analyses of the spectra were carried out over a chemical shift range from 0.4 to 4 ppm in the case of LCModel and from 0.5 to 4 ppm in the first dimension and from -0.4 to 0.4 in the second dimension for ProFit2.…”

Adaptive baseline fitting for MR spectroscopy analysis

“…19 The influence of baseline flexibility has also been explored using experimentally acquired data, with a recent study demonstrating a 15% difference in metabolite levels when comparing between a default and less flexible baseline model. 20 Baseline flexibility has also been shown to have a strong influence of the measurement of 2-hydroxyglutarate. 21 In this study, we introduce a new method to automatically determine the optimal degree of baseline flexibility for a frequency-domain spline-based fitting algorithm.…”