Computational Framework for Modeling Effects of Brain Collateral Circulation.

Abstract

During a large vessel occlusion, the survivability of the affected brain tissue depends on the ability of blood to reach the compromised territory. Consequently, the severity of ischemic strokes and the outcome of interventional treatments like thrombectomy are strongly influenced by individual anatomical features of the brain's vascular network, particularly its collateralization. However, analyzing the role of collateral circulation has proven particularly challenging, as it requires highly detailed models of arterial networks that include very small collateral vessels (~50 μm diameter). This article presents a computational framework for constructing realistic brain vascular models that capture the anatomical variability of both the circle of Willis and the pial collateral network. The methodology integrates image-based vascular reconstruction, arterial tree extension via constrained constructive optimization, and generation of leptomeningeal collateral vessels. Blood flow simulations are performed using lumped parameter models, while virtual angiograms are generated through distributed compartment modeling of transport. A virtual patient population with variable collateralization is used to study the impact of anatomical differences on collateral flow and angiographic signatures in the presence of large vessel occlusions. The results show good agreement with in vivo data and highlight features that could help infer the level of collateralization from clinical angiograms. This framework offers a foundation for improving patient-specific stroke treatment planning and understanding the hemodynamic implications of vascular variability.