A comprehensive pipeline for multi‐resolution modeling of the mitral valve: Validation, computational efficiency, and predictive capability

Abstract

Multiple studies have demonstrated that the pathological geometries unique to each patient can affect the durability of mitral valve (MV) repairs. While computational modeling of the MV is a promising approach to improve the surgical outcomes, the complex MV geometry precludes use of simplified models. Moreover, the lack of complete in-vivo geometric information presents significant challenges in the development of patient-specific computational models. There is thus a need to determine the level of detail necessary for predictive MV models. To address this issue, we have developed a novel pipeline for building attribute-rich computational models of MV with varying fidelity directly from the in-vitro imaging data. The approach combines high-resolution geometric information from loaded and unloaded states to achieve a high level of anatomic detail, followed by mapping and parametric embedding of tissue attributes to build a high resolution, attribute-rich computational models. Subsequent lower resolution models were then developed, and evaluated by comparing the displacements and surface strains to those extracted from the imaging data. We then identified the critical levels of fidelity for building predictive MV models in the dilated and repaired states. We demonstrated that a model with a feature size of ~5 mm and mesh size of ~1 mm was sufficient to predict the overall MV shape, stress, and strain distributions with high accuracy. However, we also noted that more detailed models were found to be needed to simulate microstructural events. We conclude that the developed pipeline enables sufficiently complex models for biomechanical simulations of MV in normal, dilated, repaired states.

A noninvasive method for the determination of in vivo mitral valve leaflet strains

“…Methodologies for in vitro MV imaging, image processing, and FE mesh generation have been described previously in detail . Briefly, three freshly excised ovine MV specimens were sutured to an adjustable annulus holder by a ford interlocking stitch and mounted into an extensively validated cylindrical left heart simulator .…”

“…To establish material point correspondence between the open and closed states, we used a hyperelastic morphing approach in conjunction with iterative landmark registration and thereby determined the initial and final locations of each fiducial marker . From this displacement field, we obtained the deformation gradient tensor F across the entire MV leaflet surface.…”

“…This procedure yielded a fully parameterized spline representation of the leaflet surface. We then discretized the surface uniformly using a Poisson‐disk sampling method, tuning the sampling density to arrive at a 1‐mm spatial resolution . Lastly, we reconstructed the surface from the new sample points using a ball‐pivoting algorithm and smoothed the resulting mesh using Taubin's method .…”

“…The framework used for modeling MV closure with FE has been described previously . Briefly, FE simulations were performed using the commercial software package Abaqus 2017 (Dassault Systèmes), configured to use a Newton‐Raphson quasistatic solver with explicit central difference integration and automatic time stepping.…”

“…The leaflet medial surface was discretized using first‐order isoparametric triangular elements and assigned a shell element type (Abaqus type S3). Based on previous findings, which show that accurate valve closure simulations and strain results can be achieved with a nearly incompressible nonlinear isotropic constitutive model, we modeled the mechanical response of the tissue using where S is the second Piola‐Kirchhoff (PK2) stress tensor, S ens is the PK2 stress in a unidirectional ensemble of collagen fibers oriented along with strain , is the Green‐Lagrange strain tensor, C = F T F is the right Cauchy‐Green deformation tensor, I is the identity tensor, μ m is the modulus of the neo‐Hookean ground matrix, and results from imposing incompressibility . Further, the effective fiber ensemble response was modeled using …”