Benchtop characterization of the tricuspid valve leaflet pre-strains

FEBio FINESSE: An Open-Source Finite Element Simulation Approach to Estimate In Vivo Heart Valve Strains Using Shape Enforcement

PURPOSE

Finite element simulations are an enticing tool to evaluate heart valve function; however, patient-specific simulations derived from 3D echocardiography are hampered by several technical challenges. The objective of this work is to develop an open-source method to enforce matching between finite element simulations and in vivo image-derived heart valve geometry in the absence of patient-specific material properties, leaflet thickness, and chordae tendineae structures.

METHODS

We evaluate FEBio Finite Element Simulations with Shape Enforcement (FINESSE) using three synthetic test cases considering a range of model complexity. FINESSE is then used to estimate the in vivo valve behavior and leaflet strains for three pediatric patients.

RESULTS

Our results suggest that FINESSE can be used to enforce finite element simulations to match an image-derived surface and estimate the first principal leaflet strains within ± 0.03 strain. Key considerations include: (i) defining the user-defined penalty, (ii) omitting the leaflet commissures to improve simulation convergence, and (iii) emulating the chordae tendineae behavior via prescribed leaflet free edge motion or a chordae emulating force. In all patient-specific cases, FINESSE matched the target surface with median errors of approximately the smallest voxel dimension. Further analysis revealed valve-specific findings, such as the tricuspid valve leaflet strains of a 2-day old patient with HLHS being larger than those of two 13-year old patients.

CONCLUSIONS

FEBio FINESSE can be used to estimate patient-specific in vivo heart valve leaflet strains. The development of this open-source pipeline will enable future studies to begin linking in vivo leaflet mechanics with patient outcomes.

Investigation of direction- and age-dependent prestretch in mouse cranial dura mater

Cranial dura mater is a dense interwoven vascularized connective tissue that helps regulate neurocranial remodeling by responding to strains from the growing brain. Previous ex vivo experimentation has failed to account for the role of prestretch in the mechanical behavior of the dura. Here we aim to estimate the prestretch in mouse cranial dura mater and determine its dependency on direction and age. We performed transverse and longitudinal incisions in parietal dura excised from newborn (day ∼ 4) and mature (12 weeks) mice and calculated the ex vivo normalized incision opening (measured width over length). Then, similar incisions were simulated under isotropic stretching within Abaqus/Standard. Finally, prestretch was estimated by comparing the ex vivo and in silico normalized openings. There were no significant differences between the neonatal and adult mice when comparing cuts in the same direction, but adult mice were found to have significantly greater stretch in the anterior-posterior direction than in the medial-lateral direction, while neonatal dura was essentially isotropic. Additionally, our simulations show that increasing curvature impacts the incision opening, indicating that flat in silico models may overestimate prestretch.

An investigation of how specimen dimensions affect biaxial mechanical characterizations with CellScale BioTester and constitutive modeling of porcine tricuspid valve leaflets

Biaxial mechanical characterizations are the accepted approach to determine the mechanical response of many biological soft tissues. Although several computational and experimental studies have examined how experimental factors (e.g., clamped vs. suture mounting) affect the acquired tissue mechanical behavior, little is known about the role of specimen dimensions in data acquisition and the subsequent modeling. In this study, we combined our established mechanical characterization framework with an iterative size-reduction protocol to test the hypothesis that specimen dimensions affect the observed mechanical behavior of biaxial characterizations. Our findings indicated that there were non-significant differences in the peak equibiaxial stretches of tricuspid valve leaflets across four specimen dimensions ranging from 4.5×4.5mm to 9 × 9mm. Further analyses revealed that there were significant differences in the low-tensile modulus of the circumferential tissue direction. These differences resulted in significantly different constitutive model parameters for the Tong-Fung model between different specimen dimensions of the posterior and septal leaflets. Overall, our findings demonstrate that specimen dimensions play an important role in experimental characterizations, but not necessarily in constitutive modeling of soft tissue mechanical behavior during biaxial testing with the commercial CellScale BioTester.

A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves

A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. Forty-six samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May-Newman model was the most probable model for the porcine aortic valve data. When the samples were further grouped into different cusp types, the May-Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee-Sacks model and the May-Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode's amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to other tissue types, and has the potential to provide a structured and rational way of making simulations population-based.