Linking Geometry, Hemodynamics, and Clot Risk in Carotid Webs: A Patient-Specific CFD Study

Carotid webs (CaWs) are thin, shelf-like protrusions of fibrous tissue typically located along the posterior wall of the internal carotid artery near the bifurcation. Although often subtle in imaging, CaWs are increasingly recognized as an underdiagnosed cause of ischemic stroke, particularly in younger patients without traditional risk factors. Prior studies have observed that CaWs disturb local flow, generating recirculation zones and regions of low wall shear stress. However, it remains unclear how the morphology of CaWs influences blood flow patterns and contributes to thrombus formation. We developed a patient-specific computational framework that integrates image-based geometry reconstruction with pulsatile CFD simulations. Simulations were performed in OpenFOAM to resolve detailed flow features. During analysis, we computed the time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT)–across cases with and without CaWs. These metrics revealed disturbed flow patterns due to CaWs, including low shear and recirculation zones downstream of the web. However, they proved insufficient for reliably detecting the presence of CaWs or differentiating their severity. To address this, we introduce a hemodynamic metric that incorporates spatial and temporal flow features, emphasizing pressure gradients, reverse flow duration, and separation behavior—providing improved sensitivity to CaW-induced flow disruption. In parallel, we define geometric metrics: lumen volume ratio, cross-sectional area gradient, and centerline deviation, to quantify the structural impact of CaWs along the vessel. Unlike conventional shape descriptors such as web height, length, or angle, which rely on manual measurement and are subject to observer variability, these integral metrics are automated, reproducible, and more robust to uncertainty. We establish a relationship between the geometric features and metrics to validate across patient cases.