Hemodynamic Characterisation of Type B Aortic Dissection: A Patient Specific CFD Model Validated in Vitro via PIV
Please login to view abstract download link
Aortic dissection is characterised by a tear in the intimal layer of the aorta, allowing blood to enter the vessel wall, separate its layers and form a false lumen. The condition is estimated to affect approximately 6 individuals per 100,000 per year [1], [2]. In 29–35% of cases, the dissection originates in the aortic segment distal to the left subclavian artery, a condition referred to as type B aortic dissection (TBAD). Current clinical decision-making relies primarily on clinical and morphological descriptors [3]; however, the underlying biomechanical properties of the aortic wall and the complex hemodynamic loads that exceed interlayer adhesive strength remain only partially understood [4]. This gap in knowledge limits insight into the mechanisms driving disease progression and clinical outcomes. This study investigates hemodynamics of a patient-specific TBAD, using a combined computational–experimental framework. The dissection was modelled assuming rigid-wall and Newtonian blood flow, and simulated using transient computational fluid dynamics (CFD) using the open source software SimVascular, applying physiological conditions. For validation, a phantom of the dissected portion of the aorta fabricated from optically transparent silicone was tested in a pulse duplicator system (ViVitro), and the flow patterns were quantified by means of Particle Image Velocimetry (PIV). The experimental velocity fields showed strong agreement with CFD predictions, matching the timing, location, and spatial characteristics of the flow. Both approaches revealed a backward-flow phenomenon confined to the false lumen, along with recirculation zones during late systole. Such oscillatory flow within the false lumen may promote blood stasis, potentially leading to thrombus formation. The validated CFD model provides detailed pressure and velocity distributions that clarify the mechanisms underlying transient backward flow, serving as a robust platform for patient-specific and parametric studies aimed at risk assessment, thrombosis prediction, and treatment planning.
