Do you want to publish a course? Click here

Fluid structure interaction: Insights into biomechanical implications of endograft after thoracic endovascular aortic repair

177   0   0.0 ( 0 )
 Added by Yonghui Qiao
 Publication date 2021
  fields Physics
and research's language is English




Ask ChatGPT about the research

Thoracic endovascular aortic repair (TEVAR) has developed to be the most effective treatment for aortic diseases. The objective of this study is to evaluate the biomechanical implications of the implanted endograft after TEVAR. We present a novel image-based, patient-specific, fluid-structure computational framework. The geometries of blood, endograft, and aortic wall were reconstructed based on clinical images. Patient-specific measurement data was collected to determine the parameters of the three-element Windkessel. We designed three postoperative scenarios with rigid wall assumption, blood-wall interaction, blood-endograft-wall interplay, respectively, where a two-way fluid-structure interaction (FSI) method was applied to predict the deformation of the composite stent-wall. Results show that flow energy loss (EL) during a cardiac cycle is underestimated by the rigid wall assumption. The complete storage and release process of blood flow energy, which consists of four phases is firstly captured. The implantation of the endograft would weaken the buffer function of the aorta and reduce mean EL by 19.1%. The closed curve area of wall pressure and aortic volume could indicate the EL caused by the interaction between blood flow and wall deformation. Both the FSI and endograft have a slight effect on wall shear stress-related-indices. The deformability of the composite stent-wall region is remarkably limited by the endograft. Our results highlight the importance of considering both the FSI method and the biomechanical implications of endograft to acquire physiologically-accurate hemodynamics in post-TEVAR computational studies and that neglecting the effect of the endograft would overestimate the blood flow EL and aortic deformability.



rate research

Read More

178 - Yonghui Qiao , Le Mao , Yan Wang 2021
Thoracic endovascular aortic repair (TEVAR) has become the standard treatment of a variety of aortic pathologies. The objective of this study is to evaluate the hemodynamic effects of stent-graft introducer sheath during TEVAR. Three idealized representative diseased aortas of aortic aneurysm, coarctation of the aorta, and aortic dissection were designed. Computational fluid dynamics studies were performed in the above idealized aortic geometries. An introducer sheath routinely used in the clinic was virtually-delivered into diseased aortas. Comparative analysis was carried out to evaluate the hemodynamic effects of the introducer sheath. Results show that the blood flow to the supra-aortic branches would increase above 9% due to the obstruction of the introducer sheath. The region exposed to high endothelial cell activation potential (ECAP) expands in the scenarios of coarctation of the aorta and aortic dissection, which indicates that the probability of thrombus formation may increase during TEVAR. The pressure magnitude in peak systole shows an obvious rise and a similar phenomenon is not observed in early diastole. The blood viscosity in the aortic arch and descending aorta is remarkably altered by the introducer sheath. The uneven viscosity distribution confirms the necessity of using non-Newtonian models and high viscosity region with high ECAP further promotes thrombosis. Our results highlight the hemodynamic effects of stent-graft introducer sheath during TEVAR, which may associate with perioperative complications.
Endovascular sealing is a new technique for the repair of abdominal aortic aneurysms. Commercially available in Europe since~2013, it takes a revolutionary approach to aneurysm repair through minimally invasive techniques. Although aneurysm sealing may be thought as more stable than conventional endovascular stent graft repairs, post-implantation movement of the endoprosthesis has been described, potentially leading to late complications. The paper presents for the first time a model, which explains the nature of forces, in static and dynamic regimes, acting on sealed abdominal aortic aneurysms, with references to real case studies. It is shown that elastic deformation of the aorta and of the endoprosthesis induced by static forces and vibrations during daily activities can potentially promote undesired movements of the endovascular sealing structure.
This paper presents a new method for modeling the mechanics of the aortic valve, and simulates its interaction with blood. As much as possible, the model construction is based on first principles, but such that the model is consistent with experimental observations. We require that tension in the leaflets must support a pressure, then derive a system of partial differential equations governing its mechanical equilibrium. The solution to these differential equations is referred to as the predicted loaded configuration; it includes the loaded leaflet geometry, fiber orientations and tensions needed to support the prescribed load. From this configuration, we derive a reference configuration and constitutive law. In fluid-structure interaction simulations with the immersed boundary method, the model seals reliably under physiological pressures, and opens freely over multiple cardiac cycles. Further, model closure is robust to extreme hypo- and hypertensive pressures. Then, exploiting the unique features of this model construction, we conduct experiments on reference configurations, constitutive laws, and gross morphology. These experiments suggest the following conclusions, which are directly applicable to the design of prosthetic aortic valves. (i) The loaded geometry, tensions and tangent moduli primarily determine model function. (ii) Alterations to the reference configuration have little effect if the predicted loaded configuration is identical. (iii) The leaflets must have sufficiently nonlinear material response to function over a variety of pressures. (iv) Valve performance is highly sensitive to free edge length and leaflet height. For future use, our aortic valve modeling framework offers flexibility in patient-specific models of cardiac flow.
A patient-specific fluid-structure interaction (FSI) model of a phase-contrast magnetic resonance angiography (PC-MRA) imaged arteriovenous fistula is presented. The numerical model is developed and simulated using a commercial multiphysics simulation package where a semi-implicit FSI coupling scheme combines a finite volume method blood flow model and a finite element method vessel wall model. A pulsatile mass-flow boundary condition is prescribed at the artery inlet of the model, and a three-element Windkessel model at the artery and vein outlets. The FSI model is freely available for analysis and extension. This work shows the effectiveness of combining a number of stabilisation techniques to simultaneously overcome the added-mass effect and optimise the efficiency of the overall model. The PC-MRA data, fluid model, and FSI model results show almost identical flow features in the fistula; this applies in particular to a flow recirculation region in the vein that could potentially lead to fistula failure.
While the evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations, a theoretical understanding of this complex process remains elusive. Here, we build a deep learning framework to learn this non-linear relationship, and develop techniques to physically interpret the learnt mapping. A three-dimensional convolutional neural network (CNN) is trained to predict the mass of dark matter halos from the initial conditions. We find no change in the predictive accuracy of the model if we retrain the model removing anisotropic information from the inputs. This suggests that the features learnt by the CNN are equivalent to spherical averages over the initial conditions. Our results indicate that interpretable deep learning frameworks can provide a powerful tool for extracting insight into cosmological structure formation.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا