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A comparison between simulated and experimental basis sets for assessing short-TE in vivo ~1H MRS data at 1.5 T

机译:在1.5 T下评估体内短期TE〜1H MRS数据的模拟和实验基础集之间的比较

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A number of algorithms designed to determine metabolite concentrations from in vivo ~1H MRS require a collection of single metabolite spectra, known as a basis set, which can be obtained experimentally or by simulation. It has been assumed that basis sets can be used interchangeably, but no systematic study has investigated the effects of small variations in basis functions on the metabolite values obtained. The aim of this study was to compare the results of simulated with experimental basis sets when used to fit short-TE ~1H MRS data of variable quality at 1.5 T. Two hundred and twelve paediatric brain tumour spectra were included in the analysis, and each was analysed twice with LCModel? using a simulated and experimental basis set. To determine the influence of data quality on quantification, each spectrum was assessed and 152 were classified as being of 'good' quality. Bland-Altman statistics were used to measure the agreement between the two basis sets for all available spectra and only 'good'-quality spectra. Monte-Carlo simulations were performed to investigate the influence of minor shifts in metabolite frequencies on metabolite concentration estimates. All metabolites showed good agreement between the two basis sets, and the average metabolite limits of agreement were approximately ±3.84 mM for all available data and ±0.99 mM for good-quality data. Errors obtained from the Monte-Carlo analysis were found to be more accurate than the Cramer-Rao lower bounds (CRLB) for 12 of 15 metabolites when metabolite frequency shifting was considered. For the majority of purposes, a level of agreement of ±0.99 mM between simulated and experimental basis sets is sufficiently small for them to be used interchangeably. Multiple analyses using slightly modified basis sets may be useful in estimating fitting errors, which are not predicted by CRLBs.
机译:旨在从体内〜1H MRS确定代谢物浓度的许多算法需要收集单个代谢物光谱(称为基础集),可以通过实验或模拟获得。已经假定基础集可以互换使用,但是没有系统的研究来研究基础功能的微小变化对获得的代谢物值的影响。这项研究的目的是比较当用于拟合1.5 T下质量可变的短TE〜1H MRS数据时,与实验基础集的模拟结果。分析中包括了212个小儿脑肿瘤光谱,每个用LCModel分析了两次?使用模拟和实验基础集。为了确定数据质量对定量的影响,评估了每个光谱,并将152个质量分类为“良好”。使用Bland-Altman统计量来测量所有可用光谱和仅“​​优质”光谱的两个基集之间的一致性。进行了蒙特卡洛模拟,以研究代谢物频率的微小变化对代谢物浓度估算值的影响。所有代谢物在两个基础组之间显示出良好的一致性,对于所有可用数据,一致性的平均代谢限度约为±3.84 mM,而对于高质量数据,其均值约为±0.99 mM。当考虑代谢物频移时,发现从蒙特卡洛分析获得的误差比15种代谢物中的12种的Cramer-Rao下限(CRLB)更准确。出于大多数目的,模拟和实验基础集之间的一致性水平为±0.99 mM,足以使其互换使用。使用稍微修改的基集进行的多次分析可能有助于估计拟合误差,而CRLB不会预测这些误差。

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