A computational tool to support pre-operative planning of stentless aortic valve implant

Impact of nickel-titanium super-elastic material properties on the mechanical performance of self-expandable transcatheter aortic valves

Self-expandable transcatheter aortic valves (TAVs) elastically resume their initial shape when implanted without the need for balloon inflation by virtue of the nickel-titanium (NiTi) frame super-elastic properties. Experimental findings suggest that NiTi mechanical properties can vary markedly because of a strong dependence on the chemical composition and processing operations. In this context, this study presents a computational framework to investigate the impact of the NiTi super-elastic material properties on the TAV mechanical performance. Finite element (FE) analyses of TAV implantation were performed considering two different TAV frames and three idealized aortic root anatomies, evaluating the device mechanical response in terms of pullout force magnitude exerted by the TAV frame and peak maximum principal stress within the aortic root. The widely adopted NiTi constitute model by Auricchio and Taylor (1997) was used. A multi-parametric sensitivity analysis and a multi-objective optimization of the TAV mechanical performance were conducted in relation to the parameters of the NiTi constitutive model. The results highlighted that: five NiTi material model parameters (E_A, σ_tL^S, σ_tU^S, σ_tU^E and σ_cL^S) are significantly correlated with the FE outputs; the TAV frame geometry and aortic root anatomy have a marginal effect on the level of influence of each NiTi material parameter; NiTi alloy candidates with pareto-optimal characteristics in terms of TAV mechanical performance can be successfully identified. In conclusion, the proposed computational framework supports the TAV design phase, providing information on the relationship between the super-elastic behavior of the supplied NiTi alloys and the device mechanical response.

Biomechanics of Transcatheter Aortic Valve Implant

Transcatheter aortic valve implantation (TAVI) has grown exponentially within the cardiology and cardiac surgical spheres. It has now become a routine approach for treating aortic stenosis. Several concerns have been raised about TAVI in comparison to conventional surgical aortic valve replacement (SAVR). The primary concerns regard the longevity of the valves. Several factors have been identified which may predict poor outcomes following TAVI. To this end, the lesser-used finite element analysis (FEA) was used to quantify the properties of calcifications which affect TAVI valves. This method can also be used in conjunction with other integrated software to ascertain the functionality of these valves. Other imaging modalities such as multi-detector row computed tomography (MDCT) are now widely available, which can accurately size aortic valve annuli. This may help reduce the incidence of paravalvular leaks and regurgitation which may necessitate further intervention. Structural valve degeneration (SVD) remains a key factor, with varying results from current studies. The true incidence of SVD in TAVI compared to SAVR remains unclear due to the lack of long-term data. It is now widely accepted that both are part of the armamentarium and are not mutually exclusive. Decision making in terms of appropriate interventions should be undertaken via shared decision making involving heart teams.

Automatic estimation of aortic and mitral valve displacements in dynamic CTA with 4D graph-cuts

The location of the mitral and aortic valves in dynamic cardiac imaging is useful for extracting functional derived parameters such as ejection fraction, valve excursions, and global longitudinal strain, and when performing anatomical structures tracking using slice following or valve intervention's planning. Completely automatic segmentation methods are still challenging tasks because of their fast movements and the different positions that prevent good visibility of the leaflets along the full cardiac cycle. In this article, we propose a processing pipeline to track the displacement of the aortic and mitral valve annuli from high-resolution cardiac four-dimensional computed tomographic angiography (4D-CTA). The proposed method is based on the dynamic separation of left ventricle, left atrium and aorta using statistical shape modeling and an energy minimization algorithm based on graph-cuts and has been evaluated on a set of 15 electrocardiography-gated 4D-CTAs. We report a mean agreement distance between manual annotations and our proposed method of 2.52±1.06 mm for the mitral annulus and 2.00±0.69 mm for the aortic valve annulus based on valve locations detected from manual anatomical landmarks. In addition, we show the effect of detecting the valvular planes on derived functional parameters (ejection fraction, global longitudinal strain, and excursions of the mitral and aortic valves).

Aortic expansion induces lumen narrrowing in anomalous coronary arteries: a parametric structural finite element analysis

Anomalous aortic origin of coronary arteries (AAOCA) is a congenital disease that can lead to cardiac ischemia during intense physical activity. Although AAOCA is responsible for sudden cardiac death (SCD) among young athletes and soldiers, the mechanisms underlying the coronary occlusion during physical effort still have to be clarified. The present study investigates the correlation between geometric features of the anomaly and coronary lumen narrowing under aortic root dilatations. Idealized parametric computer-aided designed (CAD) models of the aortic root with anomalous and normal coronary are created and static finite element (FE) simulations of increasing aortic root expansions are carried out. Different coronary take-off angles and intramural penetrations are investigated to assess their role on coronary lumen narrowing. Results show that increasing aortic and coronary pressures lead to lumen expansions in normal coronaries, particularly in the proximal tract, while the expansion of anomalous coronary is impaired especially at the ostium. Concerning the geometric features of the anomaly, acute take-off angles cause elongated coronary ostia, with an eccentricity increasing with aortic expansion; the impact of intramural penetration of coronary on its luminal narrowing is limited. The present study provides a proof of concept of the biomechanical reasons underlying the lumen narrowing in AAOCA during aortic expansion, promoting the role of computational simulations as a tool to assess the mechanisms of this pathology.

A framework for designing patient-specific bioprosthetic heart valves using immersogeometric fluid-structure interaction analysis

Numerous studies have suggested that medical image derived computational mechanics models could be developed to reduce mortality and morbidity due to cardiovascular diseases by allowing for patient-specific surgical planning and customized medical device design. In this work, we present a novel framework for designing prosthetic heart valves using a parametric design platform and immersogeometric fluid-structure interaction (FSI) analysis. We parameterize the leaflet geometry using several key design parameters. This allows for generating various perturbations of the leaflet design for the patient-specific aortic root reconstructed from the medical image data. Each design is analyzed using our hybrid arbitrary Lagrangian-Eulerian/immersogeometric FSI methodology, which allows us to efficiently simulate the coupling of the deforming aortic root, the parametrically designed prosthetic valves, and the surrounding blood flow under physiological conditions. A parametric study is performed to investigate the influence of the geometry on heart valve performance, indicated by the effective orifice area and the coaptation area. Finally, the FSI simulation result of a design that balances effective orifice area and coaptation area reasonably well is compared with patient-specific phase contrast magnetic resonance imaging data to demonstrate the qualitative similarity of the flow patterns in the ascending aorta.

Computational methods for the aortic heart valve and its replacements

INTRODUCTION

Replacement with a prosthetic device remains a major treatment option for the patients suffering from heart valve disease, with prevalence growing resulting from an ageing population. While the most popular replacement heart valve continues to be the bioprosthetic heart valve (BHV), its durability remains limited. There is thus a continued need to develop a general understanding of the underlying mechanisms limiting BHV durability to facilitate development of a more durable prosthesis. In this regard, computational models can play a pivotal role as they can evaluate our understanding of the underlying mechanisms and be used to optimize designs that may not always be intuitive. Areas covered: This review covers recent progress in computational models for the simulation of BHV, with a focus on aortic valve (AV) replacement. Recent contributions in valve geometry, leaflet material models, novel methods for numerical simulation, and applications to BHV optimization are discussed. This information should serve not only to infer reliable and dependable BHV function, but also to establish guidelines and insight for the design of future prosthetic valves by analyzing the influence of design, hemodynamics and tissue mechanics. Expert commentary: The paradigm of predictive modeling of heart valve prosthesis are becoming a reality which can simultaneously improve clinical outcomes and reduce costs. It can also lead to patient-specific valve design.

A patient-specific aortic valve model based on moving resistive immersed implicit surfaces

In this paper, we propose a full computational framework to simulate the hemodynamics in the aorta including the valve. Closed and open valve surfaces, as well as the lumen aorta, are reconstructed directly from medical images using new ad hoc algorithms, allowing a patient-specific simulation. The fluid dynamics problem that accounts from the movement of the valve is solved by a new 3D-0D fluid-structure interaction model in which the valve surface is implicitly represented through level set functions, yielding, in the Navier-Stokes equations, a resistive penalization term enforcing the blood to adhere to the valve leaflets. The dynamics of the valve between its closed and open position is modeled using a reduced geometric 0D model. At the discrete level, a finite element formulation is used and the SUPG stabilization is extended to include the resistive term in the Navier-Stokes equations. Then, after time discretization, the 3D fluid and 0D valve models are coupled through a staggered approach. This computational framework, applied to a patient-specific geometry and data, allows to simulate the movement of the valve, the sharp pressure jump occurring across the leaflets, and the blood flow pattern inside the aorta.

Coupled Simulation of Heart Valves: Applications to Clinical Practice

The last few decades have seen great advances in the understanding of heart valves, and consequently, in the development of novel treatment modalities and surgical procedures for valves afflicted by disease. This is due in part to the profound advancements in computing technology and noninvasive medical imaging techniques that have made it possible to numerically model the complex heart valve systems characterized by distinct features at different length scales and various interacting processes. In this article, we highlight the importance of explicitly coupling these multiple scales and diverse processes to accurately simulate the true behavior of the heart valves, in health and disease. We examine some of the computational modeling studies that have a direct consequence on clinical practice.

Enhancing physiologic simulations using supervised learning on coarse mesh solutions

Computational modelling of physical and biochemical processes has emerged as a means of evaluating medical devices, offering new insights that explain current performance, inform future designs and even enable personalized use. Yet resource limitations force one to compromise with reduced order computational models and idealized assumptions that yield either qualitative descriptions or approximate, quantitative solutions to problems of interest. Considering endovascular drug delivery as an exemplary scenario, we used a supervised machine learning framework to process data generated from low fidelity coarse meshes and predict high fidelity solutions on refined mesh configurations. We considered two models simulating drug delivery to the arterial wall: (i) two-dimensional drug-coated balloons and (ii) three-dimensional drug-eluting stents. Simulations were performed on computational mesh configurations of increasing density. Supervised learners based on Gaussian process modelling were constructed from combinations of coarse mesh setting solutions of drug concentrations and nearest neighbourhood distance information as inputs, and higher fidelity mesh solutions as outputs. These learners were then used as computationally inexpensive surrogates to extend predictions using low fidelity information to higher levels of mesh refinement. The cross-validated, supervised learner-based predictions improved fidelity as compared with computational simulations performed at coarse level meshes--a result consistent across all outputs and computational models considered. Supervised learning on coarse mesh solutions can augment traditional physics-based modelling of complex physiologic phenomena. By obtaining efficient solutions at a fraction of the computational cost, this framework has the potential to transform how modelling approaches can be applied in the evaluation of medical technologies and their real-time administration in an increasingly personalized fashion.

Simulation of transcatheter aortic valve implantation through patient-specific finite element analysis: two clinical cases

Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure introduced to treat aortic valve stenosis in elder patients. Its clinical outcomes are strictly related to patient selection, operator skills, and dedicated pre-procedural planning based on accurate medical imaging analysis. The goal of this work is to define a finite element framework to realistically reproduce TAVI and evaluate the impact of aortic root anatomy on procedure outcomes starting from two real patient datasets. Patient-specific aortic root models including native leaflets, calcific plaques extracted from medical images, and an accurate stent geometry based on micro-tomography reconstruction are key aspects included in the present study. Through the proposed simulation strategy we observe that, in both patients, stent apposition significantly induces anatomical configuration changes, while it leads to different stress distributions on the aortic wall. Moreover, for one patient, a possible risk of paravalvular leakage has been found while an asymmetric coaptation occurs in both investigated cases. Post-operative clinical data, that have been analyzed to prove reliability of the performed simulations, show a good agreement with analysis results. The proposed work thus represents a further step towards the use of realistic computer-based simulations of TAVI procedures, aiming at improving the efficacy of the operation technique and supporting device optimization.

Quantification of structural compliance of aged human and porcine aortic root tissues

The structural compliance of the aortic root has a significant implication for valve procedures such as transcatheter aortic valve implantation and valve-sparing aortic root replacement. However, a detailed quantification of human aortic root structural compliance, particularly in different regions, has been incomplete. In this study, the structural properties of human aortic roots (81 ± 8.74 years, n = 10) were characterized and compared with those of porcine ones (6-9 months, n = 10) using a vessel pressure-inflation test. The test involved tracking three-dimensional deformation of the markers affixed on the different surface regions of the aortic roots, including the three sinuses: the noncoronary sinus (NCS), the left-coronary sinus (LCS), and the right-coronary sinus (RCS), and at three regions along the longitudinal direction of each sinus: the upper sinus (US), the middle sinus (MS), and the lower sinus (LS), and the ascending aorta (AA) region above the NCS. We found that tissue stiffness in physiological pressure range was similar among the three human sinuses. A variation in regional structural stiffness of human aorta was observed. In the circumferential direction, the LS regions were the stiffest in the LCS and RCS, whereas NCS had relatively uniform stiffness. In the longitudinal direction, the human AA regions were more compliant than all sinuses. There was a significant difference in tissue stiffness between human and porcine aortic tissues, suggesting that the mechanical properties of porcine tissues may not be analogous to aged human ones.

Patient-specific modeling of biomechanical interaction in transcatheter aortic valve deployment

The objective of this study was to develop a patient-specific computational model to quantify the biomechanical interaction between the transcatheter aortic valve (TAV) stent and the stenotic aortic valve during TAV intervention. Finite element models of a patient-specific stenotic aortic valve were reconstructed from multi-slice computed tomography (MSCT) scans, and TAV stent deployment into the aortic root was simulated. Three initial aortic root geometries of this patient were analyzed: (a) aortic root geometry directly reconstructed from MSCT scans, (b) aortic root geometry at the rapid right ventricle pacing phase, and (c) aortic root geometry with surrounding myocardial tissue. The simulation results demonstrated that stress, strain, and contact forces of the aortic root model directly reconstructed from MSCT scans were significantly lower than those of the model at the rapid ventricular pacing phase. Moreover, the presence of surrounding myocardium slightly increased the mechanical responses. Peak stresses and strains were observed around the calcified regions in the leaflets, suggesting the calcified leaflets helped secure the stent in position. In addition, these elevated stresses induced during TAV stent deployment indicated a possibility of tissue tearing and breakdown of calcium deposits, which might lead to an increased risk of stroke. The potential of paravalvular leak and occlusion of coronary ostia can be evaluated from simulated post-deployment aortic root geometries. The developed computational models could be a valuable tool for pre-operative planning of TAV intervention and facilitate next generation TAV device design.

Finite element modeling of mitral valve dynamic deformation using patient-specific multi-slices computed tomography scans

The objective of this study was to develop a patient-specific finite element (FE) model of a human mitral valve. The geometry of the mitral valve was reconstructed from multi-slice computed tomography (MSCT) scans at middle diastole with distinguishable mitral leaflet thickness, chordal origins, chordal insertion points, and papillary muscle locations. Mitral annulus and papillary muscle dynamic motions were also quantified from MSCT scans and prescribed as boundary conditions for the FE simulation. Material properties of the human mitral leaflet tissues were obtained from biaxial tests and characterized by an anisotropic hyperelastic material model. In vivo dynamic closing of the mitral valve was simulated. The closed shape of the mitral valve output from the simulation was similar to the mitral valve geometry reconstructed from MSCT images at middle systole. Forces from the anterolateral and posteromedial papillary muscle groups at middle systole were 4.51 N and 5.17 N, respectively. The average maximum principal stress of the midsection of the anterior mitral leaflet was approximately 160 kPa at the systolic peak. Results demonstrated that the developed FE model could closely replicate in vivo mitral valve dynamic motion during middle diastole and systole. This model may serve as a basis for utilizing computational simulations to obtain a better understanding of mitral valve mechanics, disease and surgical repair.