Angiographic Signatures of Brain Collateralization
- 1Bioengineering Department George Mason University Fairfax Virginia USA
- 2Department of Interventional Neuroradiology University of California Los Angeles Los Angeles California USA
Abstract
The severity of ischemic strokes, the likelihood of successful endovascular treatment, and subsequent clinical patient outcomes are all strongly influenced by the degree of collateralization of the brain arterial network. Although several scoring systems based on different imaging modalities have been developed to assess brain collateralization, they are subjective and quite coarse and do not capture many nuances of the underlying collateral vessel distribution. This article introduces a methodology for quantitatively and objectively assessing the degree of collateralization of the brain. The strategy is based on a regional analysis of time‐density curves extracted from dynamic angiographic image sequences. To investigate the dependence of the extracted metrics on the underlying collateralization in a controlled manner, a computational study is carried out by generating virtual patients with prescribed collateralization characteristics and conducting blood flow and transport simulations to create virtual angiographic sequences. The virtual angiograms of patients with large vessel occlusions and different collateral vessel distributions are used to generate “angiographic signatures” reflective of the underlying collateralization. The results show that angiographic signatures created from virtual angiograms exhibit consistent characteristics with angiographic signatures of actual stroke patients with different levels of collateralization. These findings indicate that these angiographic signatures can be used to assess the underlying collateralization of the brain and identify patients and conditions associated with adverse treatment outcomes.
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BibTeX
@article{mut2026angiographic,
title = {{Angiographic Signatures of Brain Collateralization}},
author = {Fernando Mut and Aseem Pradhan and Laurel Marsh and Naoki Kaneko and Juan R. Cebral},
journal = {International journal for numerical methods in biomedical engineering : Duplicate, marked for deletion},
year = {2026},
month = nov,
doi = {10.1002/cnm.70232},
url = {https://europepmc.org/article/PMC/PMC13730812},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Angiographic Signatures of Brain Collateralization
AU - Fernando Mut
AU - Aseem Pradhan
AU - Laurel Marsh
AU - Naoki Kaneko
AU - Juan R. Cebral
PY - 2026
DA - 2026/11/01
JO - International journal for numerical methods in biomedical engineering : Duplicate, marked for deletion
DO - 10.1002/cnm.70232
UR - https://europepmc.org/article/PMC/PMC13730812
AB - The severity of ischemic strokes, the likelihood of successful endovascular treatment, and subsequent clinical patient outcomes are all strongly influenced by the degree of collateralization of the brain arterial network. Although several scoring systems based on different imaging modalities have been developed to assess brain collateralization, they are subjective and quite coarse and do not capture many nuances of the underlying collateral vessel distribution. This article introduces a methodology for quantitatively and objectively assessing the degree of collateralization of the brain. The strategy is based on a regional analysis of time‐density curves extracted from dynamic angiographic image sequences. To investigate the dependence of the extracted metrics on the underlying collateralization in a controlled manner, a computational study is carried out by generating virtual patients with prescribed collateralization characteristics and conducting blood flow and transport simulations to create virtual angiographic sequences. The virtual angiograms of patients with large vessel occlusions and different collateral vessel distributions are used to generate “angiographic signatures” reflective of the underlying collateralization. The results show that angiographic signatures created from virtual angiograms exhibit consistent characteristics with angiographic signatures of actual stroke patients with different levels of collateralization. These findings indicate that these angiographic signatures can be used to assess the underlying collateralization of the brain and identify patients and conditions associated with adverse treatment outcomes.
SN - 2040-7939
SN - 2040-7947
ER -