Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD.
F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD.
bioRxiv. 2023 Jun 15:2023.02.10.528046. doi: 10.1101/2023.02.10.528046.
Proton ( H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T relaxation times are established based upon a meta-analyses of healthy and diseased brains.
质子（ H）磁共振波谱（MRS）是一种非侵入性工具，能够定量测量脑代谢物浓度。该领域优先考虑标准化和可及性，因此开发了通用脉冲序列、方法共识建议，并开发了开源分析软件包。目前面临的一个挑战是使用真实数据进行方法验证。由于几乎没有真实数据可供测量，因此数据模拟已成为一种重要工具。代谢物测量的多样化文献使得很难定义模拟中使用的范围。特别是对于深度学习和机器学习算法的开发，模拟必须能够生成准确的光谱，捕捉数据的所有细微差别。因此，我们试图确定可用于数据模拟和参考估计的脑代谢物的生理范围和弛豫率。我们使用系统评价和荟萃分析的首选报告项目（PRISMA）指南，确定了相关的 MRS 研究文章，并创建了一个包含方法、结果和其他文章信息的开源数据库，作为资源。使用该数据库，基于健康和患病大脑的荟萃分析，建立了代谢物浓度和 T 弛豫时间的预期值和范围。
