Dell'Orco Andrea, Riemann Layla Tabea, Ellison Stephen L R, Aydin Semiha, Göschel Laura, Ittermann Bernd, Tietze Anna, Scheel Michael, Fillmer Ariane
Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Institute of Neuroradiology, Berlin, Germany.
Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Berlin, Germany.
NMR Biomed. 2025 Jan;38(1):e5299. doi: 10.1002/nbm.5299.
To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 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.
为提高在3T和7T场强下代谢物定量的可靠性，我们提出了一种用于脑氢磁共振波谱的新型参数化大分子定量模型（PRaMM），该模型将大分子峰强度的比值用作软约束。在3T和7T场强下，从6名健康志愿者的三个不同脑区采集了全谱和代谢物抑制谱，这些脑区的灰质和白质比例不同。代谢物抑制谱用于识别高度相关的大分子信号贡献并估计其强度比值。然后，这些比值被用作所提出的PRaMM模型中全谱定量的软约束。通过与单组分大分子模型和大分子减法技术进行比较，对PRaMM模型进行了验证。此外，还研究了PRaMM模型与其他方法相比对重复性和再现性的影响。在所有三个研究的脑区中，所开发的PRaMM模型的性能均优于其他两种方法。代谢物浓度的几个估计值及其克拉美罗下界受PRaMM模型再现性的影响，并通过在第二个重复采集数据集上评估该方法来测试所获得浓度的重复性。尽管观察到的对这两个指标的影响不显著，但PRaMM方法的拟合质量指标得到了改善（p≤0.0001）。最小可检测变化范围为0.5-1.9mM，几乎所有临床相关代谢物的变异系数百分比均低于10%。此外，排除了潜在的过度参数化问题。在此，我们开发了PRaMM模型，一种用于改进代谢物定量的方法，并提出了一种从临床角度研究大分子背景及其各个成分作用的方法。
