Speaker
Description
Introduction: Computational fluid dynamics (CFD) models are widely used to study haemodynamics in the cardiovascular system. One-dimensional (1-D) CFD models provide an efficient framework for simulating pulse wave propagation in large arterial networks and generating in silico datasets for physiological analysis. In peripheral artery disease (PAD), arterial stenoses reduce blood supply to the lower limbs and may lead to critical limb ischaemia. Although 1-D CFD models have been applied to simulate arterial wave dynamics, many PAD simulations neglect key haemodynamic mechanisms, including energy dissipation across stenoses and the influence of collateral circulation. The profunda femoris collateral artery can provide alternative flow pathways that influence distal perfusion.
This study investigates the influence of collateral circulation and stenosis energy-loss modelling on haemodynamic predictions in a 1-D CFD model of the lower-limb arterial network.
Methods: An established 116-artery 1-D CFD model of the systemic circulation was extended to include the profunda femoris collateral pathway, resulting in a 130-artery representation of the lower-limb circulation. Stenoses of varying severity and configuration were introduced in the superficial femoral artery. Different stenosis energy-loss models were tested and incorporated in the arterial network. Pressure, flow rate, and perfusion distribution were compared between simulations with and without collateral pathways.
Results: Under healthy conditions, simulated pulse waveforms showed less than 1% root mean square error compared with the original 116-artery model and were consistent with reported physiological data. In PAD simulations, collateral pathways improved distal perfusion, particularly in severe and sequential stenoses. Critical stenosis thresholds for each PAD type were identified, and the role of collateral pathways was evaluated.
Conclusion: Collateral circulation and stenosis energy dissipation significantly influence haemodynamic predictions in PAD simulations. Incorporating these mechanisms improves the physiological realism of 1-D CFD arterial network models and supports the generation of physiologically consistent datasets for diagnostic modelling.