This thesis investigates computational modelling of blood flow in the lower extremities to inform clinical decision-making for deep vein thrombosis (DVT) and post-thrombotic syndrome (PTS). Despite high incidence of recurrent DVT (~30%) and PTS (20-50%), current strategies such as thrombus extraction and venous stenting rely on limited quantitative evidence. Clinical assessment is often based on qualitative evaluation of venous inflow and outflow, lacking standardised metrics to guide intervention.
This thesis offers a novel application of physics-based CFD to iliofemoral venous haemodynamics—a domain where computational work is limited. By combining progressively more complex models with sensitivity analysis, it addresses clinically relevant questions about thrombosis risk that have not been explored systematically before.
Computational fluid dynamics (CFD) provides a framework for quantifying haemodynamics and identifying flow disturbances that predispose to thrombosis. Although widely used in arterial research, CFD remains underexplored in venous modelling, particularly in DVT. This work hypothesises that numerical models grounded in the physics of lower-limb circulation can improve understanding of DVT-related haemodynamics and support the identification of clinically relevant biomarkers, within uncertainty quantification and sensitivity analysis.
This research spans generic 0D and 3D models, anatomically informed simulations, and patient-specific reconstructions. Sensitivity analyses assess the influence of anatomical and physiological parameters on predicted pressure and flow metrics. In idealised models, no single dominant factor is identified. Patient-specific modelling, based on imaging from a case of chronic DVT, suggests that anatomical variation may exert a greater influence on haemodynamic outcomes than inflow variability, though broader conclusions require analysis across additional anatomical variants.
The proposed workflow enables detailed haemodynamic reporting and supports exploration of shear-based risk metrics, such as wall shear stress and oscillatory shear index. This work contributes to both theoretical understanding and clinical translation, highlighting the potential of CFD to inform personalised treatment strategies for DVT and PTS.
