Short-TE proton MRS is used to study metabolism in the human brain. Common analysis methods model the data as linear combination of metabolite basis spectra. This large-scale multi-site study compares the levels of the four major metabolite complexes in short-TE spectra estimated by three linear-combination modelling (LCM) algorithms. 277 medial parietal lobe short-TE PRESS spectra (TE = 35 ms) from a recent 3T multi-site study were pre-processed with the Osprey software. The resulting spectra were modelled with Osprey, Tarquin and LCModel, using the same three vendor-specific basis sets (GE, Philips, and Siemens) for each algorithm. Levels of total N-acetylaspartate (tNAA), total choline (tCho), myo-inositol (mI), and glutamate+glutamine (Glx) were quantified with respect to total creatine (tCr). Group means and CVs of metabolite estimates agreed well for tNAA and tCho across vendors and algorithms, but substantially less so for Glx and mI, with mI systematically estimated lower by Tarquin. The cohort mean correlation coefficient for all pairs of LCM algorithms across all datasets and metabolites was R²=0.39, indicating generally only moderate agreement of individual metabolite estimates between algorithms. There was a significant correlation between local baseline amplitude and metabolite estimates (cohort mean R²=0.10). While mean estimates of major metabolite complexes broadly agree between linear-combination modelling algorithms at group level, correlations between algorithms are only weak-to-moderate, despite standardized pre-processing, a large sample of young, healthy and cooperative subjects, and high spectral quality. These findings raise concerns about the comparability of MRS studies, which typically use one LCM software and much smaller sample sizes.

中文翻译：

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短TE质子谱的不同线性组合建模算法的比较

短TE质子MRS用于研究人脑中的新陈代谢。常用的分析方法将数据建模为代谢物基础光谱的线性组合。这项大规模的多站点研究比较了通过三种线性组合建模（LCM）算法估算的短TE光谱中四种主要代谢物的水平。使用Osprey软件对来自最近的3T多站点研究的277个内侧顶叶短TE PRESS光谱（TE = 35 ms）进行了预处理。使用Osprey，Tarquin和LCModel对每种算法使用相同的三个特定于供应商的基本集（GE，Philips和Siemens）对所得光谱进行建模。相对于总肌酸（tCr）定量了总N-乙酰天门冬氨酸（tNAA），总胆碱（tCho），肌醇（mI）和谷氨酸+谷氨酰胺（Glx）的水平。在不同供应商和算法中，tNAA和tCho的代谢物估计值的组均值和CV相吻合，但对于Glx和mI而言，tIAA和tCho却相差甚远，而Tarquin系统地估计mI较低。所有数据集和代谢物上所有LCM算法对的同类平均相关系数为R² = 0.39，通常表明算法之间各个代谢物估计值的适度一致。局部基线幅度与代谢物估计值之间存在显着相关性（队列均值R ² = 0.10）。尽管主要代谢物复合物的平均估计值在组水平的线性组合建模算法之间基本一致，但尽管标准化的预处理，大量年轻，健康和合作受试者的样本以及高光谱，这些算法之间的相关性还是弱到中等的。质量。这些发现引起了人们对MRS研究可比性的担忧，MRS研究通常使用一种LCM软件和较小的样本量。