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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.

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Summary
This summary is machine-generated.

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.

Keywords:
DSC-MRI imagingPythonglioblastoma (GBM)neuroimagingperfusion analysisstroke

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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.