Comparison of seven modelling algorithms for γ-aminobutyric acid-edited proton magnetic resonance spectroscopy.
Comparison of seven modelling algorithms for γ-aminobutyric acid-edited proton magnetic resonance spectroscopy.
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编辑后的MRS序列被广泛用于研究人脑中的γ-氨基丁酸。有几种算法可用于对这些数据进行建模，通过峰拟合或基谱的线性组合来得出代谢物浓度估计。本研究使用从大型多站点研究中获得的数据，比较了七种这样的算法。来自20个地点的222名受试者的GABA编辑(GABA+，TE = 68 MS Mega-Press)数据通过标准化管道处理，然后使用FSL-MRS、Gannet、Amares、Quest、LC Model、Osprey和Tarquin进行建模，在适当的情况下使用标准化的供应商特定基准集(针对GE、飞利浦和西门子)。在参考了代谢物估计(对水或肌酸)之后，观察到在不同供应商的硬件上获取的数据集在规模上的系统性差异，呈现在不同的算法上。不同算法之间的比例差异也被观察到。使用代谢物估计和体素组织分数之间的相关性作为基准，大多数算法被发现在检测GABA+的差异方面同样有效。所有算法的组间相关性显示，GABA+估计的单个评分者的一致性约为0.38，表明符合程度中等。在加入了一个基组成分，明确地对观察到的3.0ppm GABA峰背后的大分子信号进行建模后，单一评分者的一致性提高到了0.44。离散算法对之间的相关性各不相同，在某些情况下非常弱，令人担忧。我们的发现强调了在不同算法中就适当的建模参数达成共识的必要性，以及对个别研究中采用的参数进行详细报告的必要性，以确保不同研究之间的结果的重复性和有意义的比较。来自20个研究地点的222名健康成年人的GABA编辑的MRS数据使用7种不同的算法进行建模，并最大限度地标准化了预处理和先验知识。所有算法的一致性都是中等的，尽管扫描仪供应商和量化算法的某些组合存在一些系统性的差异。对3ppm左右的共同编辑的大分子信号的显式处理提高了一致性；这一点和基线建模成为区分结果的主要因素，值得进一步研究。
Edited MRS sequences are widely used for studying γ‐aminobutyric acid (GABA) in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multisite study. GABA‐edited (GABA+, TE = 68 ms MEGA‐PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL‐MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor‐specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An interclass correlation across all algorithms showed single‐rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single‐rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies. GABA‐edited MRS data from 222 healthy adults across 20 research sites were modelled using seven different algorithms, with preprocessing and prior knowledge standardised to the maximum extent practical. Moderate agreement was seen across all algorithms, albeit with some systematic differences of magnitude for certain combinations of scanner vendor and quantification algorithm. Explicit handling of coedited macromolecule signals around 3 ppm improved concordance; this and baseline modelling emerged as major factors differentiating outcomes, and worthy of further investigation.
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