Vascular smooth muscle cell (VSMC) plasticity is implicated in extracellular matrix (ECM) turnover and arterial failure. The osteochondrocytic phenotypes of synthetic VSMCs are thought to drive glycosaminoglycan (GAG) accumulation and swelling typically seen in connective tissue disease and hypertension. A central question is whether this phenotype switching under non-homeostatic conditions is a cause or effect of those conditions. We implement a cause–effect association between ECM damage, lost cell mechanosensitivity, and cell phenotype modulation using the Constrained Mixture Model, to simulate the evolution of VSMC population over time. We modelled a cylindrical bi-layer of media and adventitia of a mouse common carotid artery and simulated remodelling in response to initially compromised ECM, concurrent with varying degrees of hypertension. In normo- and moderately hypertensive ECM disruption, physiological remodelling restores mechanical homeostasis to cells with slightly altered mechanical properties. Alternatively, severe hypertension yields complete medial degeneration. Complete loss of stored elastic energy is observed, with stiffened arteries yielding characteristically high pulse wave velocities (PWVs). Early intervention recovering hypertensive to normotensive pressure, as well as enhanced adventitial collagen turnover, are shown to prevent medial degeneration. Our model thus offers a tool to better understand the relationship between ECM damage, arterial failure, and hypertension.

Background and Objective: Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. Methods: Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. Results: Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. Conclusions: We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.

Introduction: Thoracic endovascular aortic repair (TEVAR) of the arch is challenging given its complex geometry and the involvement of supra-aortic arteries. Different branched endografts have been designed for use in this region, but their haemodynamic performance and the risk for post-intervention complications are not yet clear. This study aims to examine aortic haemodynamics and biomechanical conditions following TVAR treatment of an aortic arch aneurysm with a two-component single-branched endograft. Methods: Computational fluid dynamics and finite element analysis were applied to a patient-specific case at different stages: pre-intervention, post-intervention and follow-up. Physiologically accurate boundary conditions were used based on available clinical information. Results: Computational results obtained from the post-intervention model confirmed technical success of the procedure in restoring normal flow to the arch. Simulations of the follow-up model, where boundary conditions were modified to reflect change in supra-aortic vessel perfusion observed on the follow-up scan, predicted normal flow patterns but high levels of wall stress (up to 1.3M MPa) and increased displacement forces in regions at risk of compromising device stability. This might have contributed to the suspected endoleaks or device migration identified at the final follow up. Discussion: Our study demonstrated that detailed haemodynamic and biomechanical analysis can help identify possible causes for post-TEVAR complications in a patient-specific setting. Further refinement and validation of the computational workflow will allow personalised assessment to aid in surgical planning and clinical decision making.