OBJECTIVES The effect of aortic haemodynamics on arterial wall properties in ascending thoracic aortic aneurysms (ATAAs) is not well understood. We aim to delineate the relationship between shear forces along the aortic wall and loco-regional biomechanical properties associated with the risk of aortic dissection. METHODS Five patients with ATAA underwent preoperative magnetic resonance angiogram and four-dimensional magnetic resonance imaging. From these scans, haemodynamic models were constructed to estimate maximum wall shear stress (WSS), maximum time-averaged WSS, average oscillating shear index and average relative residence time. Fourteen resected aortic samples from these patients underwent bi-axial tensile testing to determine energy loss (ΔUL) and elastic modulus (E10) in the longitudinal (ΔULlong, E10long) and circumferential (ΔULcirc, E10circ) directions and the anisotropic index (AI) for each parameter. Nine resected aortic samples underwent peel testing to determine the delamination strength (Sd). Haemodynamic indices were then correlated to the biomechanical properties. RESULTS A positive correlation was found between maximum WSS and ΔULlong rs=0.75, P = 0.002 and AIΔUL (rs=0.68, P=0.01). Increasing maximum time-averaged WSS was found to be associated with increasing ΔULlong (rs=0.73, P = 0.003) and AIΔUL (rs=0.62, P=0.02). Average oscillating shear index positively correlated with Sd (rs=0.73,P=0.04). No significant relationship was found between any haemodynamic index and E10, or between relative residence time and any biomechanical property. CONCLUSIONS Shear forces at the wall of ATAAs are associated with local degradation of arterial wall viscoelastic hysteresis (ΔUL) and delamination strength, a surrogate for aortic dissection. Haemodynamic indices may provide insights into aortic wall integrity, ultimately leading to novel metrics for assessing risks associated with ATAAs.

Computational fluid dynamics were used to assess hemodynamic changes in an actively rupturing abdominal aortic aneurysm (AAA) over a 9-day period. Active migration of contrast from the lumen into the thickest region of intraluminal thrombus (ILT) was demonstrated until it ultimately breached the adventitial layer. Four days after symptom onset, there was a discrete disruption of adventitial calcium with bleb formation at the site of future rupture. Rupture occurred in a region of low wall shear stress and was associated with a marked increase in AAA diameter from 6.6 to 8.4 cm. The cross-sectional area of the flow lumen increased across all time points from 6.28 to 12.08 cm². The increase in luminal area preceded the increase in AAA diameter and was characterized by an overall deceleration in recirculation flow velocity with a coinciding increase in flow velocity penetrating the ILT. We show that there are significant hemodynamic and structural changes in the AAA flow lumen in advance of any appreciable increase in aortic diameter or rupture. The significant increase in AAA diameter with rupture suggests that AAA may actually rupture at smaller sizes than those measured on day of rupture. These findings have implications for algorithms the predict AAA rupture risk.

Computational Fluid Dynamics (CFD) has been widely used to predict and understand cardiovascular flows. However, the accuracy of CFD predictions depends on faithful reconstruction of patient vascular anatomy and accurate patient-specific inlet and outlet boundary conditions. 4-Dimensional Magnetic Resonance Imaging (4D MRI) can provide patient-specific data to obtain the required geometry and time-dependent flow boundary conditions for CFD simulations, and can further be used to validate CFD predictions. This work presents a framework to combine both spatiotemporal 4D MRI data and patient monitoring data with CFD simulation workflows. To assist practitioners, all aspects of the modelling workflow, from geometry reconstruction to results post-processing, are illustrated and compared using three software packages (ANSYS, COMSOL, SimVascular) to predict hemodynamics in the thoracic aorta. A sensitivity analysis with respect to inlet boundary conditions is presented. Results highlight the importance of 4D MRI data for improving the accuracy of flow predictions on the ascending aorta and the aortic arch. In contrast, simulation results for the descending aorta are less sensitive to the patient-specific inlet boundary conditions.

Computational Fluid Dynamics (CFD) is widely used in both scientific and industrial contexts to provide valuable insights into cardiovascular flows. CFD supports the development of medical devices, describes complex flow physical phenomena associated with disease generation and progression, and even informs surgical planning. Non-invasive technologies such as 4D MRI provide detailed information about blood flow for a given patient, yet CFD allows higher spatial and temporal resolution less invasively. However, the advantages of CFD methods can only be realized through faithful geometry reconstruction, high-quality mesh generation, and suitable definition of patient-specific inlet and outlet boundary conditions. In this regard, 4D MRI measurements can provide the required data to calibrate and validate patient-specific CFD models. Hence, the combination of 4D MRI and CFD is crucial for accurate and efficient in-silico cardiovascular flow predictions for patient-specific geometries. Multiple CFD software, such as ANSYS, COMSOL, and SimVascular, have been widely used in published research works, yet the graphical user interfaces of these software packages have no explicit provisions for leveraging spatio-temporal 4D MRI data. Here, we present a framework to create accurate patient-specific CFD simulations leveraging spatiotemporal 4D MRI data and patient monitoring data. We discuss all aspects of patient-specific modeling, including geometry reconstruction, meshing, numerical simulation, and post-processing of results, focusing on the suitability of each software package and the ease with which the presented workflow can be implemented with the software.