Macromolecule Modelling for Improved Metabolite Quantification Using Short Echo Time Brain <sup>1</sup>H‐MRS at 3 T and 7 T: The PRaMM Model

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain <sup>1</sup>H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single-component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared with those other methods was investigated. The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. Although the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p ≤ 0.0001). Minimally detectable changes are in the range 0.5-1.9 mM, and the percentage coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Here, the PRaMM model, a method for an improved quantification of metabolites, was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

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Publication Details

Journal
NMR in Biomedicine
Published
2024-12-19
DOI
https://doi.org/10.1002/nbm.5299
Citations
1
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.40

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Macromolecule Modelling for Improved Metabolite Quantification Using Short Echo Time Brain 1H‐MRS at 3 T and 7 T: The PRaMM Model

Andrea Dell’Orco, Michael Scheel, Ariane Fillmer, Layla Tabea Riemann et al.
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article

Macromolecule Modelling for Improved Metabolite Quantification Using Short Echo Time Brain 1H‐MRS at 3 T and 7 T: The PRaMM Model

Andrea Dell’Orco, Michael Scheel, Ariane Fillmer, Layla Tabea Riemann, Semiha Aydın, Bernd Ittermann, Stephen L. R. Ellison, Laura Göschel, Anna Tietze
article en
1 citations

Abstract

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain <sup>1</sup>H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single-component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared with those other methods was investigated. The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. Although the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p ≤ 0.0001). Minimally detectable changes are in the range 0.5-1.9 mM, and the percentage coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Here, the PRaMM model, a method for an improved quantification of metabolites, was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

NMR in BiomedicineVol. 38(1)
Physikalisch-Technische Bundesanstalt (DE), Universität Hamburg (DE), Humboldt-Universität zu Berlin (DE), University Medical Center Hamburg-Eppendorf (DE), Freie Universität Berlin (DE), Charité - Universitätsmedizin Berlin (DE)
European Association of National Metrology Institutes, European Commission, European Metrology Programme for Innovation and Research
Openalex Percentile: Top 31%
Advanced MRI Techniques and Applications
0.40
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