Perfusion-weighted software written in Python for DSC-MRI analysis

Abstract

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.

Keywords: DSC-MRI imaging; Python; glioblastoma (GBM); neuroimaging; perfusion analysis; stroke.

Figure 1

Diagram of the main steps implemented in our software to extract the perfusion-weighted images.

Figure 2

Flowchart of data set screening.

Figure 3

Examples of mean cerebral l blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR) images generated with our tool for the different animal models. Anatomic MRI of the rat brain model used is detailed with T2-wi or DWI (ADC) (red arrows indicate the ischemic lesion and presence of glioma).

Figure 4

(A) Comparison of mean cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR) in selected regions of interest (ROIs) of healthy SD rats, during hypoperfusion model, and mannitol injection. (B) Mean CBF, CBV, MTT, SR, and PSR in contralateral hemisphere, penumbra, and core ROIs during occlusion in an ischemic stroke animal model. (C) Mean CBF, CBV, MTT, SR, and PSR in the peripherical and core ROIs for glioblastoma model at 10 and 20 days after the surgery.

Figure 5

Bland–Altman plots for CBV, CBF, and MTT for our results vs. literature data regarding SD healthy rats (Adam et al., ; Perles-Barbacaru and Lahrech, ; Rouine et al., ; Lee et al., 2021). Dashed lines represent the bias, +95% (upper line) and −95% (lower line) of the limits of agreements.

Figure 6

Bland–Altman plots for CBV, CBF, and MTT for our results vs. literature data regarding the ischemic stroke animal model (Thomas et al., ; Zhang et al., ; Boisserand et al., ; Livingston et al., ; Tsai et al., 2021). Dashed lines represent the bias, +95% (upper line) and −95% (lower line) of the limits of agreements.

Figure 7

Bland–Altman plots for CBV, CBF, and MTT for our results vs. literature data regarding the glioblastoma animal model (García-Palmero et al., ; Stokes et al., ; Gonawala et al., ; Clément et al., 2021). Dashed lines represent the bias, +95% (upper line) and −95% (lower line) of the limits of agreements.