CFD Analysis of Patient-Specific and Idealized Models for Predicting Stenosis Locations and Hemodynamic Disturbances in Peripheral Arterial Disease

Peripheral arterial disease (PAD), a condition caused by atherosclerosis, poses significant cardiovascular risks by disrupting blood flow. Computational fluid dynamics offers insights into vascular remodeling mechanisms, leveraging patient-specific anatomical data from computed tomography angiography (CTA) to enhance the accuracy of blood flow analysis. This study aims to evaluate the capability of an idealized arterial model in simulating hemodynamic parameters and blood flow patterns by comparing it with patient-specific geometries. Additionally, the study investigates the impact of stenosis at different locations within the femoral artery: upstream, downstream, and at the Profunda on flow disturbances and downstream regions. Blood flow was modeled as a Newtonian fluid, assuming a constant viscosity independent of shear rate, which is a reasonable approximation for femoral arteries where shear rates are typically high. Both an idealized PAD geometry and a patient-specific model derived from CTA data were employed for simulations. Results showed elevated blood velocities at bifurcations, notably at the superficial femoral artery (SFA) and profunda femoral artery, with peak velocities exceeding 1.90 m/s. Regions of low wall shear stress (WSS) were identified at key branching points and along arteries such as the popliteal and tibial arteries. The idealized model effectively replicated patient-specific flow patterns. Upstream stenosis caused severe flow disturbances, with velocities up to 3.9 m/s and Reynolds numbers (Re) of 1272 in the mainstream region, disrupting flow recovery. Downstream stenosis caused severe disturbances, with Re of 1835 beyond the bifurcations, whereas profunda stenosis had minimal effect, maintaining Re below 1000. This study emphasizes the importance of patient-specific anatomical factors in predicting stenosis and highlights the utility of idealized models for generating hemodynamic profiles. These findings enhance the pre-treatment planning and management methods for PAD patients.