October 1, 2008
Journal Article

Characterization of Highly Nonlinear and Anisotropic Vascular Tissues from Experimental Inflation Data: a Validation Study Towards the Use of Clinical Data for In-vivo Modeling and Analysis

Abstract

A new ¯nite element (FE) modeling approach is proposed to model blood vessel by using data from blood vessel during in°ation instead of using data from biaxial test to estimate the tissue property. A particular constitutive equation is used to model the tissue property as a dispersed transverse isotropic hyperelastic material under both single-layer and multi-layer assumption for comparison. In order to compensate the drawback of using this type of data that does not provide su±cient information to estimate the tissue property in axial direction, the axial/circumferential elastic moduli ratio is being constrained in a typical range. The semi-global inverse method is applied with the pressure-diameter test data from porcine thoracic aorta to esti- mate the optimal tissue property. The simulated data of all cases is then compared with the biaxial test data. It is found that the single-layer model without the con- straint ¯ts well with the pressure-diameter test data but not with the biaxial test data at the axial direction, so it is suitable only for pressure-diameter analysis. The multi-layer model with the constraint ¯ts well with the pressure-diameter data and also the biaxial test data, so it is suitable to estimate the tissue property and for stress-strain analysis. The constitutive equation and the semi-global inverse method are found e±cient in this study. We believe that this new approach that uses this type of data, such as magnetic resonance imaging (MRI) data that could be ob- tained non-invasive in vivo, is time-e±cient and would be bene¯cial for pre-surgical analysis.

Revised: January 20, 2009 | Published: October 1, 2008

Citation

Kinon C., C. Kinon, B. Fata, and D.R. Einstein. 2008. Characterization of Highly Nonlinear and Anisotropic Vascular Tissues from Experimental Inflation Data: a Validation Study Towards the Use of Clinical Data for In-vivo Modeling and Analysis. Annals of Biomedical Engineering 36, no. 10:1668-1680. PNNL-SA-52382. doi:10.1007/s10439-008-9541-9