Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Philips Ingenia and Achieva collected 61 and 46 in vivo 1H magnetic resonance (MR) spectra of healthy participants, respectively, from the brain region of pregenual anterior cingulate cortex from September to October 2021. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation and reasonability between the two quantification methods. Results: Fifteen healthy volunteers (12 females and 3 males, age range: 21-35 years, mean age/standard deviation = 27.4/3.9 years) were recruited. The analyses of Bland-Altman, Pearson correlation and reasonability showed high to good consistency and very strong to moderate correlation between the two methods for quantification of total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins) (relative half interval of limits of agreement = 3.04%, 9.3%, and 18.5%, respectively; Pearson correlation coefficient r = 0.775, 0.927, and 0.469, respectively). In addition, quantification results of QNet are more likely to be closer to the previous reported average values than those of LCModel. Conclusion: There were high or good degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally has more reasonable quantification than LCModel.

翻译：目的：本研究旨在通过易用的智能云计算平台CloudBrain-MRS，对深度学习定量方法QNet与经典方法LCModel在人体脑部磁共振波谱代谢物定量结果进行统计学比较。材料与方法：本回顾性研究使用两台3T磁共振扫描仪（Philips Ingenia与Achieva），于2021年9月至10月期间分别采集了61例与46例健康受试者膝前扣带回脑区的活体¹H磁共振波谱。采用Bland-Altman分析、Pearson相关性分析及合理性评估，以检验两种定量方法的一致性程度、线性相关性与结果合理性。结果：共招募15名健康志愿者（12名女性，3名男性，年龄范围21-35岁，平均年龄/标准差=27.4/3.9岁）。Bland-Altman分析、Pearson相关性分析与合理性评估表明，两种方法对总N-乙酰天冬氨酸、总胆碱及肌醇的定量结果具有高至良好的一致性及极强至中等的相关性（一致性界限的相对半宽分别为3.04%、9.3%与18.5%；Pearson相关系数r分别为0.775、0.927与0.469）。此外，QNet的定量结果较LCModel更接近既往文献报道的平均值。结论：QNet与LCModel对总N-乙酰天冬氨酸、总胆碱及肌醇的定量结果具有高或良好的一致性，且QNet的定量结果普遍较LCModel更为合理。