Characterization of the Highly Nonlinear and Anisotropic Vascular Tissues from Experimental Inflation Data: A Validation Study Toward the Use of Clinical Data for In-Vivo Modeling and Analysis

Abstract

In this study, we investigate whether an inverse modeling approach can be used to characterize vascular tissue behavior from experimental data of blood vessels subjected at various levels of internal pressure and axial stretch that mimics clinical data. In-vivo condition of blood vessel with either constant or variable axial response is considered. To compensate for the limitation of this data that does not provide axial force information, a new concept to constrain the ratio of axial to circumferential elastic moduli to a typical range is proposed. Vessel wall constitutive behavior was modeled with a transversely isotropic hyperelastic equation that accounts for dispersed collagen fibers, and both single-layer and bi-layer models were implemented for examination. The possibility of obtaining the fiber orientation in this approach was also evaluated. The characterized behavior was validated with an independent pipette-aspiration biaxial data on the same samples. It was found that homogenous assumption is an over-simplification. The constrained bi-layer model was in excellent agreement with both types of experimental data. Fiber angle is needed to be pre-defined to avoid covariance problem. Finally, our approach is relatively invariant to any particular axial response. Therefore, we believe that inverse modeling approach is suitable for in-vivo characterization.

Anisotropic and inhomogeneous mechanical characteristics of the retina

“…However, the effects are not significant. Blood vessels are typically stiffer in the axial direction than in the circumferential direction (Chen et al, 2008;Silver, 1987).…”

A Computationally Efficient Formal Optimization of Regional Myocardial Contractility in a Sheep With Left Ventricular Aneurysm

“…This study employed the gradient-free optimization approach called the successive response surface method (SRSM), which is better able to reach the global minimum [39, 40]. It is based on the iterative construction and minimization of response surfaces that have been fitted onto the residuals between the measured and FE predicted strains.…”

Validation of computational models in biomechanics

“…For biological tissues, mean values were used as inputs and sensitivity analysis tested the influence of their variance on the system. The most basic exploratory models of cell and vascular response contained one or two materials, so sensitivity analysis was simplified considerably [47, 53, 55, 56]. Complex computational studies of bone, cartilage and ligament deformation involving multiple materials, loading scenarios and geometries scaled up the degree of sensitivity analysis [1, 38, 39, 42].…”

“…The boundary conditions generally agreed well between experiment and simulation, since extension of model applicability was not of immediate concern in the basic system validation. A few models were tested with the intention of future clinical use in patient diagnostics, implantation and outcome [1, 42, 46, 47, 54, 55, 58], but require further analysis before extension into the clinical environment.…”