Objective: Computational modelling of ascending thoracic aortic aneurysms (ATAA) typically assumes zero-displacement at the model's inlet. In this study we incorporated different types of aortic root motion into fluid-structure interaction (FSI) models representing an ATAA and a healthy aorta to examine their impacts on wall stress and wall shear stress (WSS) predictions. Methods: Five types of boundary conditions were specified at the inlet of the solid domain: (a) zero-displacement constraints, (b) longitudinal displacement, (c) in-plane displacement, (d) combined longitudinal and in-plane displacement, and (e) rotation. The aortic walls were prestressed and modelled as anisotropic hyperelastic materials. A transitional turbulence model was employed to simulate the non-Newtonian blood flow, together with patient-specific boundary conditions. Results: Combined longitudinal and in-plane displacement at the aortic root increased regions with elevated maximum principal stress (MPS > 250 kPa) by 331% for the healthy aorta, and 57.1% for the ATAA model. Peak wall stress showed modest increases by 11.4% and 14% in the ATAA model and healthy aorta, respectively. Combined longitudinal and in-plane displacement increased the area of extremely high WSS regions (>20 Pa) by 20.5% in the ATAA model, primarily in the ascending aorta. For the healthy aorta, rotation had the most notable impact on WSS, reducing the area of elevated WSS regions (>7 Pa) by 18.8%. Conclusion: Our results highlight the importance of incorporating aortic root motion into FSI models for more accurate prediction of aortic wall stress and WSS. This would enhance patient-specific risk stratification for patients with ATAA.

This study aimed to characterize the altered hemodynamics and wall mechanics in ascending thoracic aortic aneurysms (ATAA) by employing fully coupled two-way fluid–structure interaction (FSI) analyses. Our FSI models incorporated hyperelastic wall mechanical properties, prestress, and patient-specific inlet velocity profiles (IVP) extracted from 4D flow magnetic resonance imaging (MRI). By performing FSI analyses on 7 patient-specific ATAA models and 6 healthy aortas, the primary objective of the study was to compare hemodynamic and biomechanical features in ATAA versus healthy controls. A secondary objective was to examine the need for 4D flow MRI-derived IVP in FSI simulations by comparing results with those using two commonly adopted idealized IVPs: Flat-IVP and Para-IVP for selected cases. Our results show that, compared to the healthy aortas, the ATAA models exhibited highly disturbed blood flow in the ascending aorta. Consequently, maximum turbulent kinetic energy (TKE) at peak systole (155.0 ± 188.4 Pa) and maximum time-averaged wall shear stress (TAWSS) (8.6 ± 6.5 Pa) were significantly higher in the ATAA cohort, compared to 0.6 ± 0.5 Pa and 2.8 ± 0.7 Pa in the healthy aortas. Peak wall stress was also nearly doubled in the ATAA group (414 ± 108 kPa vs. 215 ± 31 kPa). Additionally, comparisons of simulation results across models with different IVPs underscore the importance of prescribing 3D-IVP at the inlet, especially for ATAA cases. Using idealized IVPs in two selected ATAA models (P1 and P7) substantially reduced the maximum TKE from 571 Pa to 0.01 Pa (Flat-IVP) and 0.02 Pa (Para-IVP) in P1 and from 73 Pa to 0.01 Pa (Flat-IVP) and 0.08 Pa (Para-IVP) in P7, while the maximum TAWSS in the ascending aorta decreased from 9.6 Pa to 0.7 Pa (Flat-IVP) and 0.9 Pa (Para-IVP) in P1, and from 3.6 Pa to 1.2 Pa and 0.9 Pa, respectively, in P7. Moreover, idealized IVPs also caused the peak wall stress to reduce by up to 11.5% in P1 with severe aortic valve stenosis, and by up to 2% in P7 with mild aortic regurgitation. These results highlight the importance of FSI simulations combined with 4D flow MRI in capturing realistic hemodynamic and biomechanical changes in aneurysmal aortas.

Purpose: Finite element analysis (FEA) has been used to predict wall stress in ascending thoracic aortic aneurysm (ATAA) in order to evaluate risk of dissection or rupture. Patient-specific FEA requires detailed information on ATAA geometry, loading conditions, material properties, and wall thickness. Unfortunately, measuring aortic wall thickness and mechanical properties non-invasively poses a significant challenge, necessitating the use of non-patient-specific data in most FE simulations. This study aimed to assess the impact of employing non-patient-specific material properties and wall thickness on ATAA wall stress predictions. Methods: FE simulations were performed on 13 ATAA geometries reconstructed from computed tomography angiography (CTA) images. Patient-specific material properties and wall thicknesses were made available from a previous study where uniaxial tensile testing was performed on tissue samples obtained from the same patients. The ATAA wall models were discretised with hexahedral elements and prestressed. For each ATAA model, FE simulations were conducted using patient-specific material properties and wall thicknesses, and group-mean values derived from all tissue samples included in the same experimental study. Literature-based material property and wall thickness were also obtained from the literature and applied to 4 representative cases. Additional FE simulations were performed on these 4 cases by employing group-mean and literature-based wall thicknesses. Results: FE simulations using the group-mean material property produced peak wall stresses comparable to those obtained using patient-specific material properties, with a mean deviation of 7.8%. Peak wall stresses differed by 20.8% and 18.7% in patients with exceptionally stiff or compliant walls, respectively. Comparison to results using literature-based material properties revealed larger discrepancies, ranging from 5.4% to 28.0% (mean 20.1%). Bland-Altman analysis showed significant discrepancies in areas of high wall stress, where wall stress obtained using patient-specific and literature-based properties differed by up to 674 kPa, compared to 227 kPa between patient-specific and group-mean properties. Regarding wall thickness, using the literature-based value resulted in even larger discrepancies in predicted peak stress, ranging from 24.2% to 30.0% (mean 27.3%). Again, using the group-mean wall thickness offered better predictions with a difference less than 5% in three out of four cases. While peak wall stresses were most affected by the choice of mechanical properties or wall thickness, the overall distribution of wall stress hardly changed. Conclusions: Our study demonstrated the importance of incorporating patient-specific material properties and wall thickness in FEA for risk prediction of aortic dissection or rupture. Our future efforts will focus on developing inverse methods for non-invasive determination of patient-specific wall material parameters and wall thickness.

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.