Strongly coupled fluid-structure interaction analysis of TEVAR with double-branched endograft for non-A non-B aortic dissection: a patient-specific case study

Thoracic endovascular aortic repair (TEVAR) with branched devices has shown its promise in the treatment of complex thoracic aortic pathologies such as chronic aortic dissection involving the aortic arch. Understanding the short-term and long-term biomechanical changes remains essential for the prediction of possible adverse events and optimising post-operative patient care. This study presents a patient-specific haemodynamic and biomechanical evaluation of a branched TEVAR device by performing two-way fluid-structure interaction (FSI) simulation. The geometric model was reconstructed from the computed tomography angiography scan, and the additional branch configurations were constructed based on the measured orientation and device-specific dimensions. The native aortic wall was modelled as an anisotropic hyperelastic material, and strongly coupled FSI simulations were conducted with three-dimensional velocity profiles at the inlet and Windkessel boundary conditions at the outlets. Our FSI simulation results captured physiologically realistic flow dynamics, wall shear stress distributions, and displacement forces (up to 14.95 N), revealing both favourable outcomes and regions of potential thrombotic risk. In particular, low wall shear stress (< 0.4 Pa) and high relative residence time (> 15 Pa^-1) were observed in the left common carotid artery branch, correlating with thrombus formation observed in a follow-up scan, whereas such regions were underestimated by the rigid-wall computational fluid dynamics simulation. Wall stress analysis revealed peak von Mises stresses exceeding 600 kPa at the proximal and distal landing zones of the stent-graft due to compliance mismatch. These findings underscore the potential value of incorporating wall compliance into the computational workflow for post-TEVAR evaluation, offering more accurate prediction of adverse events and informing patient-specific long-term monitoring strategies.