Predictive capabilities of various constitutive models for arterial tissue

Area of Science:

• Biomedical Engineering
• Materials Science
• Computational Mechanics

Background:

• Understanding the mechanical behavior of biological tissues is crucial for developing medical devices and treatments.
• Constitutive models are essential for simulating tissue mechanics, but their accuracy in predicting complex behaviors like biaxial loading needs validation.

Purpose of the Study:

• To validate four constitutive models (Holzapfel-Gasser-Ogden, Generalized Structure Tensor, Four-Fiber-Family, and Microfiber) for their ability to describe and predict the mechanical behavior of porcine aortic tissue under uniaxial and biaxial loading conditions.

Main Methods:

• Porcine aortic tissue samples underwent 2 uniaxial and 5 biaxial tensile tests, with transversal strains recorded during uniaxial tests.
• Four constitutive models were fitted to the experimental data, separately for uniaxial and biaxial datasets.
• The descriptive and predictive capabilities of each model were assessed by fitting to one loading condition and predicting the other.

Main Results:

• All four models exhibited excellent descriptive capabilities for the tested porcine aortic tissue.
• The Microfiber model showed the highest accuracy in predicting uniaxial responses from biaxial data.
• The Microfiber and Four-Fiber-Family models reasonably predicted biaxial responses from uniaxial data, while Holzapfel-Gasser-Ogden and Generalized Structure Tensor models failed.

Conclusions:

• The Holzapfel-Gasser-Ogden and Generalized Structure Tensor models are not suitable for predicting biaxial arterial wall behavior.
• The Four-Fiber-Family model emerged as the most robust among the investigated constitutive models.
• Incorporating transversal strain data from uniaxial tests enhances the robustness of constitutive models.