IntroductionDynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies in magnetic resonance imaging (MRI) provide valuable data for studying vascular cerebral pathophysiology in different rodent models of brain diseases (stroke, tumor grading, and neurodegenerative models). The extraction of these hemodynamic parameters via DSC-MRI is based on tracer kinetic modeling, which can be solved using deconvolution-based methods, among others. Most of the post-processing software used in preclinical studies is home-built and custom-designed. Its use being, in most cases, limited to the institution responsible for the development. In this study, we designed a tool that performs the hemodynamic quantification process quickly and in a reliable way for research purposes.MethodsThe DSC-MRI quantification tool, developed as a Python project, performs the basic mathematical steps to generate the parametric maps: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR). For the validation process, a data set composed of MRI rat brain scans was evaluated: i) healthy animals, ii) temporal blood–brain barrier (BBB) dysfunction, iii) cerebral chronic hypoperfusion (CCH), iv) ischemic stroke, and v) glioblastoma multiforme (GBM) models. The resulting perfusion parameters were then compared with data retrieved from the literature.ResultsA total of 30 animals were evaluated with our DSC-MRI quantification tool. In all the models, the hemodynamic parameters reported from the literature are reproduced and they are in the same range as our results. The Bland–Altman plot used to describe the agreement between our perfusion quantitative analyses and literature data regarding healthy rats, stroke, and GBM models, determined that the agreement for CBV and MTT is higher than for CBF.ConclusionAn open-source, Python-based DSC post-processing software package that performs key quantitative perfusion parameters has been developed. Regarding the different animal models used, the results obtained are consistent and in good agreement with the physiological patterns and values reported in the literature. Our development has been built in a modular framework to allow code customization or the addition of alternative algorithms not yet implemented.

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用 Python 编写的用于 DSC-MRI 分析的灌注加权软件

简介磁共振成像（MRI）中的动态磁敏感加权对比增强（DSC）灌注研究为研究不同啮齿动物脑疾病模型（中风、肿瘤分级和神经退行性模型）的血管脑病理生理学提供了有价值的数据。通过 DSC-MRI 提取这些血流动力学参数是基于示踪动力学模型，可以使用基于反卷积的方法等来解决。临床前研究中使用的大多数后处理软件都是自制和定制设计的。在大多数情况下，其使用仅限于负责开发的机构。在本研究中，我们设计了一种工具，可以出于研究目的以可靠的方式快速执行血流动力学量化过程。方法DSC-MRI 量化工具作为 Python 项目开发，执行基本数学步骤来生成参数图：脑血流量 (CBF)、脑血容量 (CBV)、平均通过时间 (MTT)、信号恢复 (SR) 和信号恢复百分比 (PSR)。在验证过程中，评估了由 MRI 大鼠脑部扫描组成的数据集：i) 健康动物，ii) 颞叶血脑屏障 (BBB) 功能障碍，iii) 脑慢性低灌注 (CCH)，iv) 缺血性中风，v) ）多形性胶质母细胞瘤（GBM）模型。然后将所得的灌注参数与从文献中检索到的数据进行比较。结果使用我们的 DSC-MRI 量化工具对总共 30 只动物进行了评估。在所有模型中，都再现了文献报道的血流动力学参数，并且它们与我们的结果在同一范围内。Bland-Altman 图用于描述我们的灌注定量分析与有关健康大鼠、中风和 GBM 模型的文献数据之间的一致性，确定 CBV 和 MTT 的一致性高于 CBF。结论一个基于 Python 的开源软件已开发出执行关键定量灌注参数的 DSC 后处理软件包。对于使用的不同动物模型，获得的结果是一致的，并且与文献中报告的生理模式和值非常一致。我们的开发建立在模块化框架中，允许代码定制或添加尚未实现的替代算法。