Effects of different macromolecular models on reproducibility of FID‐MRSI at 7T

Abstract

Purpose A properly characterized macromolecular (MM) contribution is essential for accurate metabolite quantification in FID‐MRSI. MM information can be included into the fitting model as a single component or parameterized and included over several individual MM resonances, which adds flexibility when pathologic changes are present but is prone to potential overfitting. This study investigates the effects of different MM models on MRSI reproducibility. Methods Clinically feasible, high‐resolution FID‐MRSI data were collected in ~5 min at 7 Tesla from 10 healthy volunteers and quantified via LCModel (version 6.3) with 3 basis sets, each with a different approach for how the MM signal was handled: averaged measured whole spectrum (full MM), 9 parameterized components (param MM) with soft constraints to avoid overparameterization, or without any MM information included in the fitting prior knowledge. The test–retest reproducibility of MRSI scans was assessed voxel‐wise using metabolite coefficients of variation and intraclass correlation coefficients and compared between the basis sets. Correlations of concentration estimates were investigated for the param MM fitting model. Results The full MM model provided the most reproducible quantification of total NAA, total Cho, myo‐inositol, and glutamate + glutamine ratios to total Cr (coefficients of variations ≤ 8%, intraclass correlation coefficients ≥ 0.76). Using the param MM model resulted in slightly lower reproducibility (up to +3% higher coefficients of variations, up to −0.1 decreased intraclass correlation coefficients). The quantification of the parameterized macromolecules did not affect quantification of the overlapping metabolites. Conclusion Clinically feasible FID‐MRSI with an experimentally acquired MM spectrum included in prior knowledge provides highly reproducible quantification for the most common neurometabolites in healthy volunteers. Parameterization of the MM spectrum may be preferred as a compromise between quantification accuracy and reproducibility when the MM content is expected to be pathologically altered.

High-resolution metabolic imaging of high-grade gliomas using 7T-CRT-FID-MRSI

“…There is evidence that the spectral pattern of the macromolecular background can vary in tumors. Despite promising results in healthy volunteers ( Heckova et al, 2019 , Považan et al, 2018 ), we could not reproduce these results in our patient population. Thus, we opted for a single MM contribution, which should be replaced by a more flexible parameterized MM model to account for changes in the spectral pattern of macromolecular components ( Cudalbu et al, 2020 ).…”

Intra‐session and inter‐subject variability of 3D‐FID‐MRSI using single‐echo volumetric EPI navigators at 3T

“…However, the available SNR and spectral resolution at 3T limited the quantification of lower concentrated metabolites (e.g., glutathione, γ-aminobutyric acid). Using a short acquisition delay of 0.8 ms made an improved optimization of lipid contamination via L 2 -lipid regularization and macromolecular signals 37 necessary because of their fast T 2 -relaxation.…”

Concentration and effective T2 relaxation times of macromolecules at 3T

“…The proper characterization of macromolecules is necessary to synthesize realistic data from simulation, which is necessary to optimize the choice of TE when macromolecules have not been nulled or subtracted. 15 The use of realistic macromolecular shapes in fitting has been shown to be superior 2,16 to the commonly used, but non-physically realistic, spline baseline.…”