TY - JOUR
T1 - Perfusion-weighted software written in Python for DSC-MRI analysis
AU - Fernández-Rodicio, Sabela
AU - Ferro-Costas, Gonzalo
AU - Sampedro-Viana, Ana
AU - Bazarra-Barreiros, Marcos
AU - Ferreirós, Alba
AU - López-Arias, Esteban
AU - Pérez-Mato, María
AU - Ouro, Alberto
AU - Pumar, José M.
AU - Mosqueira, Antonio J.
AU - Alonso-Alonso, María Luz
AU - Castillo, José
AU - Hervella, Pablo
AU - Iglesias-Rey, Ramón
N1 - Publisher Copyright:
Copyright © 2023 Fernández-Rodicio, Ferro-Costas, Sampedro-Viana, Bazarra-Barreiros, Ferreirós, López-Arias, Pérez-Mato, Ouro, Pumar, Mosqueira, Alonso-Alonso, Castillo, Hervella and Iglesias-Rey.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - DSC-MRI imaging
KW - glioblastoma (GBM)
KW - neuroimaging
KW - perfusion analysis
KW - Python
KW - stroke
UR - http://www.scopus.com/inward/record.url?scp=85168291682&partnerID=8YFLogxK
U2 - 10.3389/fninf.2023.1202156
DO - 10.3389/fninf.2023.1202156
M3 - Article
C2 - 37593674
AN - SCOPUS:85168291682
SN - 1662-5196
VL - 17
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
M1 - 1202156
ER -
