A parametric model for studying the aorta hemodynamics by means of the computational fluid dynamics

Impact of wall displacements on the large-scale flow coherence in ascending aorta

In the context of aortic hemodynamics, uncertainties affecting blood flow simulations hamper their translational potential as supportive technology in clinics. Computational fluid dynamics (CFD) simulations under rigid-walls assumption are largely adopted, even though the aorta contributes markedly to the systemic compliance and is characterized by a complex motion. To account for personalized wall displacements in aortic hemodynamics simulations, the moving-boundary method (MBM) has been recently proposed as a computationally convenient strategy, although its implementation requires dynamic imaging acquisitions not always available in clinics. In this study we aim to clarify the real need for introducing aortic wall displacements in CFD simulations to accurately capture the large-scale flow structures in the healthy human ascending aorta (AAo). To do that, the impact of wall displacements is analyzed using subject-specific models where two CFD simulations are performed imposing (1) rigid walls, and (2) personalized wall displacements adopting a MBM, integrating dynamic CT imaging and a mesh morphing technique based on radial basis functions. The impact of wall displacements on AAo hemodynamics is analyzed in terms of large-scale flow patterns of physiological significance, namely axial blood flow coherence (quantified applying the Complex Networks theory), secondary flows, helical flow and wall shear stress (WSS). From the comparison with rigid-wall simulations, it emerges that wall displacements have a minor impact on the AAo large-scale axial flow, but they can affect secondary flows and WSS directional changes. Overall, helical flow topology is moderately affected by aortic wall displacements, whereas helicity intensity remains almost unchanged. We conclude that CFD simulations with rigid-wall assumption can be a valid approach to study large-scale aortic flows of physiological significance.

Reducing Geometric Uncertainty in Computational Hemodynamics by Deep Learning-Assisted Parallel-Chain MCMC

Computational hemodynamic modeling has been widely used in cardiovascular research and healthcare. However, the reliability of model predictions is largely dependent on the uncertainties of modeling parameters and boundary conditions, which should be carefully quantified and further reduced with available measurements. In this work, we focus on propagating and reducing the uncertainty of vascular geometries within a Bayesian framework. A novel deep learning (DL)-assisted parallel Markov chain Monte Carlo (MCMC) method is presented to enable efficient Bayesian posterior sampling and geometric uncertainty reduction. A DL model is built to approximate the geometry-to-hemodynamic map, which is trained actively using online data collected from parallel MCMC chains and utilized for early rejection of unlikely proposals to facilitate convergence with less expensive full-order model evaluations. Numerical studies on two-dimensional aortic flows are conducted to demonstrate the effectiveness and merit of the proposed method.

A detailed analysis in thoracic aorta by means of the entropy generation rate: Prediction of the atherosclerotic lesion

A detailed numerical analysis is carried out in a real human thoracic aorta by means of the Computational Fluid Dynamics (CFD) for the prediction of the atherosclerosis lesion. Common hemodynamics parameters, such as, the oscillatory shear index (OSI) and the time average wall shear stress (TAWSS) are used for the prediction of the atherosclerosis lesion. Furthermore, the entropy generation rate is considered to obtain the main irreversibilities that occurs inside the thoracic aorta for the prediction of the atherosclerosis lesion. The model considers the blood flow inside the thoracic aorta in an unsteady state. The results show contours of velocity, streams lines, velocity profiles and the comparison of the hemodynamics parameters OSI versus TAWSS. Moreover, contours of the entropy generation rate are showed inside the aorta. The time averaged entropy generation rate (TAEGR) is obtained as a result of the entropy generation analysis. Finally, TAEGR index is compared and discussed with the common hemodynamics parameters, OSI and TAWSS. The accuracy to detect prone locations to atherosclerotic development in the real aorta using the TAEGR in comparison to the OSI and the TAWSS is in good agreement.

A Comparative Study on the Hemodynamic Performance Within Cross and Non-cross Stent-Grafts for Abdominal Aortic Aneurysms With an Angulated Neck

Objectives: Cross-limb stent grafts for endovascular aneurysm repair (EVAR) are often employed for abdominal aortic aneurysms (AAAs) with significant aortic neck angulation. Neck angulation may be coronal or sagittal; however, previous hemodynamic studies of cross-limb EVAR stent grafts (SGs) primarily utilized simplified planar neck geometries. This study examined the differences in flow patterns and hemodynamic parameters between crossed and non-crossed limb SGs at different spatial neck angulations.

Methods: Ideal models consisting of 13 cross and 13 non-cross limbs were established, with coronal and sagittal angles ranging from 0 to 90°. Computational fluid dynamics (CFD) was used to capture the hemodynamic information, and the differences were compared.

Results: With regards to the pressure drop index, the maximum difference caused by the configuration and angular direction was 4.6 and 8.0%, respectively, but the difference resulting from the change in aneurysm neck angle can reach 27.1%. With regards to the SAR-TAWSS index, the maximum difference caused by the configuration and angular direction was 7.8 and 9.8%, respectively, but the difference resulting from the change in aneurysm neck angle can reach 26.7%. In addition, when the aneurysm neck angle is lower than 45°, the configuration and angular direction significantly influence the OSI and helical flow intensity index. However, when the aneurysm neck angle is greater than 45°, the hemodynamic differences of each model at the same aneurysm neck angle are reduced.

Conclusion: The main factor affecting the hemodynamic index was the angle of the aneurysm neck, while the configuration and angular direction had little effect on the hemodynamics. Furthermore, when the aneurysm neck was greatly angulated, the cross-limb technique did not increase the risk of thrombosis.