Effects of different macromolecular models on reproducibility of FID‐MRSI at 7T. Issue 1 (8th August 2019)

• Record Type: Journal Article
• Authors: Heckova, Eva; Považan, Michal; Strasser, Bernhard; Motyka, Stanislav; Hangel, Gilbert; Hingerl, Lukas; Moser, Philipp; Lipka, Alexandra; Gruber, Stephan; Trattnig, Siegfried; Bogner, Wolfgang
• Is Part Of: Magnetic resonance in medicine. Volume 83:Issue 1(2020)
• Page Start: 12
• Page End: 21
• Publication Date: 2019-08-08
• Subjects: brain -- macromolecules -- MR spectroscopic imaging -- parameterization -- reproducibility -- ultrahigh field; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27922
• Languages: English
• ISSNs: 0740-3194

Abstract

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.