Fluid–structure interaction analysis of eccentricity and leaflet rigidity on thrombosis biomarkers in bioprosthetic aortic valve replacements

Oks, David; Samaniego, Cristóbal; Houzeaux, Guillaume; Butakoff, Constantine; Vázquez, Mariano

doi:10.1002/cnm.3649

Cited by 12 publications

(8 citation statements)

References 95 publications

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“…Thus, Equation (10) is the definition of the distance d W that is used in the first term of the cost function (Equation ( 8)). The second term of the cost function is a penalization term to avoid deformations with high "kinetic" energy, 30 where B 0,i is the N cp Â 3 matrix of momenta belonging to target geometry…”

Section: Theory Behind the Statistical Shape Modeling Frameworkmentioning

confidence: 99%

“…In silico trials could possibly speed up the clinical introduction of improved TAVI devices. Computational fluid dynamics (CFD) and fluid–structure interaction (FSI) models are typically used to simulate aortic valve dynamics before, 7,8 and after treatment 9–13 . Furthermore, finite element methods can be used to simulate contact forces between the aortic root and the TAVI valve during deployment 14–16 .…”

Section: Introductionmentioning

confidence: 99%

“…Computational fluid dynamics (CFD) and fluid-structure interaction (FSI) models are typically used to simulate aortic valve dynamics before, 7,8 and after treatment. [9][10][11][12][13] Furthermore, finite element methods can be used to simulate contact forces between the aortic root and the TAVI valve during deployment. [14][15][16] Performing in silico trials using these models requires patient-specific aortic valve geometries and boundary conditions.…”

mentioning

confidence: 99%

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Generation of synthetic aortic valve stenosis geometries for in silico trials

Verstraeten,

Hoeijmakers,

Tonino

et al. 2023

Numer Methods Biomed Eng

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In silico trials are a promising way to increase the efficiency of the development, and the time to market of cardiovascular implantable devices. The development of transcatheter aortic valve implantation (TAVI) devices, could benefit from in silico trials to overcome frequently occurring complications such as paravalvular leakage and conduction problems. To be able to perform in silico TAVI trials virtual cohorts of TAVI patients are required. In a virtual cohort, individual patients are represented by computer models that usually require patient‐specific aortic valve geometries. This study aimed to develop a virtual cohort generator that generates anatomically plausible, synthetic aortic valve stenosis geometries for in silico TAVI trials and allows for the selection of specific anatomical features that influence the occurrence of complications. To build the generator, a combination of non‐parametrical statistical shape modeling and sampling from a copula distribution was used. The developed virtual cohort generator successfully generated synthetic aortic valve stenosis geometries that are comparable with a real cohort, and therefore, are considered as being anatomically plausible. Furthermore, we were able to select specific anatomical features with a sensitivity of around 90%. The virtual cohort generator has the potential to be used by TAVI manufacturers to test their devices. Future work will involve including calcifications to the synthetic geometries, and applying high‐fidelity fluid–structure‐interaction models to perform in silico trials.

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Section: Theory Behind the Statistical Shape Modeling Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Generation of synthetic aortic valve stenosis geometries for in silico trials

Verstraeten,

Hoeijmakers,

Tonino

et al. 2023

Numer Methods Biomed Eng

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“…In principle, these can be treated by considering a structural model for the solid (leaflets of the valve and possibly its chordae tendinae) and a fluid dynamics model for the surrounding blood flow. This approach yields a coupled FSI model of the blood-valve system [24,[62][63][64][65][66][67][68][69][70], characterized by contact phenomena and fast dynamics. Thus, FSI valve models are commonly associated to a huge computational burden, to be added to the overall cost of the heart CFD simulation.…”

Section: Introductionmentioning

confidence: 99%

An electromechanics-driven fluid dynamics model for the simulation of the whole human heart

Zingaro¹,

Bucelli²,

Piersanti³

et al. 2023

Preprint

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We introduce a multiphysics and geometric multiscale computational model, suitable to describe the hemodynamics of the whole human heart, driven by a four-chamber electromechanical model. We first present a study on the calibration of the biophysically detailed RDQ20 activation model that is able to reproduce the physiological range of hemodynamic biomarkers. Then, we demonstrate that the ability of the force generation model to reproduce certain microscale mechanisms, such as the dependence of force on fiber shortening velocity, is crucial to capture the overall physiological mechanical and fluid dynamics macroscale behavior. This motivates the need for using multiscale models with high biophysical fidelity, even when the outputs of interest are relative to the macroscale. We show that the use of a high-fidelity electromechanical model, combined with a detailed calibration process, allows us to achieve a remarkable biophysical fidelity in terms of both mechanical and hemodynamic quantities. Indeed, our electromechanical-driven CFD simulationscarried out on an anatomically accurate geometry of the whole heart -provide results that match the cardiac physiology both qualitatively (in terms of flow patterns) and quantitatively (when comparing in silico results with biomarkers acquired in vivo). Moreover, we consider the pathological case of left bundle branch block, and we investigate the consequences that an electrical abnormality has on cardiac hemodynamics thanks to our multiphysics integrated model. The computational model that we propose can faithfully predict a delay and an increasing wall shear stress in the left ventricle in the pathological condition. The interaction of different physical processes in an integrated framework allows us to faithfully describe and model this pathology, by capturing and reproducing the intrinsic multiphysics nature of the human heart.

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“…41 To avoid the need of a complete remeshing of the computational domain, while maintaining a sharp description of the valve surface, different XFEM/cutFEM methods have been proposed, [42][43][44][45][46][47][48] but their use to simulate cardiac flows at the organ scale has been limited by their relatively high computational cost. On the other hand, fully Eulerian approaches, such as the immersed boundary method, [49][50][51][52][53][54][55][56][57] the fictitious domain method [58][59][60][61][62][63][64] or the Resistive Immersed Implicit Surface (RIIS) method, 21,65 hinge upon an implicit representation of the leaflets and do not require mesh conformity between the fluid domain and the valves. This allows to track the fluid-valve interface, possibly moving in time, without requiring the fluid mesh to follow the valve.…”

Section: Introductionmentioning

confidence: 99%

Modeling isovolumetric phases in cardiac flows by an Augmented Resistive Immersed Implicit Surface method

Zingaro,

Bucelli,

Fumagalli

et al. 2023

Numer Methods Biomed Eng

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A major challenge in the computational fluid dynamics modeling of the heart function is the simulation of isovolumetric phases when the hemodynamics problem is driven by a prescribed boundary displacement. During such phases, both atrioventricular and semilunar valves are closed: consequently, the ventricular pressure may not be uniquely defined, and spurious oscillations may arise in numerical simulations. These oscillations can strongly affect valve dynamics models driven by the blood flow, making unlikely to recovering physiological dynamics. Hence, prescribed opening and closing times are usually employed, or the isovolumetric phases are neglected altogether. In this article, we propose a suitable modification of the Resistive Immersed Implicit Surface (RIIS) method (Fedele et al., Biomech Model Mechanobiol 2017, 16, 1779–1803) by introducing a reaction term to correctly capture the pressure transients during isovolumetric phases. The method, that we call Augmented RIIS (ARIIS) method, extends the previously proposed ARIS method (This et al., Int J Numer Methods Biomed Eng 2020, 36, e3223) to the case of a mesh which is not body‐fitted to the valves. We test the proposed method on two different benchmark problems, including a new simplified problem that retains all the characteristics of a heart cycle. We apply the ARIIS method to a fluid dynamics simulation of a realistic left heart geometry, and we show that ARIIS allows to correctly simulate isovolumetric phases, differently from standard RIIS method. Finally, we demonstrate that by the new method the cardiac valves can open and close without prescribing any opening/closing times.

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Fluid–structure interaction analysis of eccentricity and leaflet rigidity on thrombosis biomarkers in bioprosthetic aortic valve replacements

Cited by 12 publications

References 95 publications

Generation of synthetic aortic valve stenosis geometries for in silico trials

Generation of synthetic aortic valve stenosis geometries for in silico trials

An electromechanics-driven fluid dynamics model for the simulation of the whole human heart

Modeling isovolumetric phases in cardiac flows by an Augmented Resistive Immersed Implicit Surface method

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