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Perfusion-weighted software written in Python for DSC-MRI analysis
Sabela Fernández-Rodicio1, Gonzalo Ferro-Costas2, Ana Sampedro-Viana1
1Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.
Introduction:
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.
Methods:
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.
Results:
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.
Conclusion:
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.
Insights
A new Python-based software tool enables rapid and reliable quantification of hemodynamic parameters from dynamic susceptibility-weighted contrast-enhanced (DSC) MRI perfusion studies in rodent models. This tool accurately reproduces literature values for various brain disease models, aiding preclinical research.
Area of Science:
- Neuroimaging
- Biophysics
- Medical Physics
Background:
- Dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MRI is crucial for studying cerebral vascular pathophysiology in rodent models of neurological diseases.
- Current post-processing software for DSC-MRI is often institution-specific and lacks broad accessibility.
- Standardized and reliable quantification of hemodynamic parameters is essential for advancing preclinical brain disease research.
Purpose of the Study:
- To develop an open-source, Python-based software tool for efficient and reliable quantification of hemodynamic parameters from DSC-MRI data.
- To validate the developed tool using diverse rodent models of brain diseases, including stroke, tumor, and neurodegenerative conditions.
- To provide a customizable and accessible post-processing solution for the research community.
Main Methods:
- Developed a Python software package to perform deconvolution-based kinetic modeling for DSC-MRI.
- Generated parametric maps for cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR).
- Validated the tool on a dataset of 30 rats across healthy, blood-brain barrier dysfunction, chronic hypoperfusion, ischemic stroke, and glioblastoma multiforme models.
Main Results:
- The developed DSC-MRI quantification tool successfully reproduced hemodynamic parameters consistent with literature values across all evaluated rodent models.
- Bland-Altman analysis indicated good agreement between the tool's results and literature data, particularly for CBV and MTT in healthy, stroke, and GBM models.
- The software demonstrated reliability in quantifying key perfusion parameters, essential for preclinical research.
Conclusions:
- An open-source, Python-based DSC post-processing software has been successfully developed and validated.
- The tool provides accurate and reliable quantification of hemodynamic parameters, aligning with established literature values for various brain disease models.
- The modular design facilitates customization and future algorithm integration, enhancing its utility for preclinical research.
