In vivo metabolite quantification by short echo time MR spectroscopy is a challenge for which various methods have been proposed. In this study, the reproducibility of quantification outcomes is questioned at three distinct levels: (i) between-software (LCModel and cQUEST), (ii) withinsoftware (with different parameter sets), and (iii) across software executions (when the fitting algorithm uses random seeds, like cQUEST). After running multiple quantification tasks on a dedicated platform (VIP), metrics from Bland-Altman analysis were used to assess the variability of outcomes in signals acquired on a lysolecithin rat model, from a study on demyelination. Results show substantial variations at the three levels, allowing for more potent analyses than from a single parameter set / single software point of view.
翻译:在活体代谢物的量化中,通过短回声时间MR光谱分析是一个挑战,对此提出了各种办法。在本研究中,量化结果的可复制性在三个不同层次受到质疑:(一) 软件(LCModel和CQUEST)之间,(二) 软件内部(有不同的参数组),(三) 软件执行(当安装算法使用随机种子,如CQUEST)时),在对专用平台(VIP)执行多项量化任务后,Bland-Altman分析的计量方法被用来评估从脱清线研究获得的赖索利西廷鼠模型信号的结果的变异性。结果显示,在三个层次上有很大差异,允许比单一参数组/单一软件观点进行更强有力的分析。</s>