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Computational Framework for Modeling Effects of Brain Collateral Circulation.

Fernando Mut1, Rainald Lohner2, Aseem Pradhan1

  • 1Bioengineering Department, George Mason University, Fairfax, Virginia, USA.

International Journal for Numerical Methods in Biomedical Engineering
|December 4, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a computational framework to model brain vascular networks, aiding in understanding collateral circulation during large vessel occlusions. The findings help infer collateralization from angiograms, improving stroke treatment planning.

Keywords:
arterial networksblood flowbrain collateralizationcomputational modelingischemic stroke

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Area of Science:

  • Biomedical Engineering
  • Computational Neuroscience
  • Medical Imaging

Background:

  • Collateral circulation is crucial for brain tissue survival during large vessel occlusions (LVOs).
  • Assessing collateral function is challenging due to the need for detailed vascular models including small vessels.
  • Individual variations in brain vascular anatomy significantly impact ischemic stroke severity and treatment outcomes.

Purpose of the Study:

  • To develop a computational framework for creating realistic brain vascular models with high anatomical detail.
  • To investigate the impact of variable collateralization on collateral flow and angiographic signatures in LVOs.
  • To provide a foundation for patient-specific stroke treatment planning and understanding hemodynamic variability.

Main Methods:

  • Image-based vascular reconstruction and arterial tree extension using constrained constructive optimization.
  • Generation of leptomeningeal collateral vessels and blood flow simulations with lumped parameter models.
  • Virtual angiogram generation via distributed compartment modeling and simulation on a virtual patient population.

Main Results:

  • The framework successfully models brain vascular networks, including the circle of Willis and pial collateral pathways.
  • Simulations demonstrated good agreement with in vivo data, correlating anatomical features with collateral flow.
  • Identified key features in virtual angiograms that can help infer the level of collateralization in patients.

Conclusions:

  • The developed computational framework enables realistic modeling of brain vascular anatomy and collateral circulation.
  • This tool can aid in understanding the hemodynamic consequences of vascular variability in stroke.
  • The findings support improved patient-specific stroke treatment planning and diagnosis using clinical angiograms.