Fernández-Rodicio Sabela, Ferro-Costas Gonzalo, Sampedro-Viana Ana, Bazarra-Barreiros Marcos, Ferreirós Alba, López-Arias Esteban, Pérez-Mato María, Ouro Alberto, Pumar José M, Mosqueira Antonio J, Alonso-Alonso María Luz, Castillo José, Hervella Pablo, Iglesias-Rey Ramón
Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.
Centro de Supercomputación de Galicia (CESGA), Santiago de Compostela, Spain.
Front Neuroinform. 2023 Aug 1;17:1202156. doi: 10.3389/fninf.2023.1202156. eCollection 2023.
Dynamic 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.
The 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.
A 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.
An 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.
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脑部疾病（中风、肿瘤分级和神经退行性模型）的啮齿动物模型中的脑血管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基于示踪剂动力学建模，其中可以使用基于反卷积的方法等进行求解。临床前研究中使用的大多数后处理软件都是自行构建和定制设计的。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，用于为研究目的快速且可靠地执行血流动力学量化过程。
作为一个Python项目开发的DSC-MRI量化工具执行生成参数图的基本数学步骤：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）。对于验证过程，评估了一个由MRI大鼠脑部扫描组成的数据集：i）健康动物，ii）短暂性血脑屏障（BBB）功能障碍，iii）脑慢性灌注不足（CCH），iv）缺血性中风，以及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。
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，文献报道的血流动力学参数均得到重现，且与我们的结果处于相同范围内。用于描述我们的灌注定量分析与关于健康大鼠、中风和GBM模型的文献数据之间一致性的Bland-Altman图确定，CBV和MTT的一致性高于CBF。
已开发出一个基于Python的开源DSC后处理软件包，该软件包可执行关键的定量灌注参数。对于所使用的不同动物模型，获得的结果是一致的，并且与文献中报道的生理模式和值高度吻合。我们的开发是在一个模块化框架中构建的，以允许代码定制或添加尚未实现的替代算法。
