Title
Modeling of human artery tissue with probabilistic approach
Abstract
urate modeling of biological soft tissue properties is vital for realistic medical simulation. Mechanical response of biological soft tissue always exhibits a strong variability due to the complex microstructure and different loading conditions. The inhomogeneity in human artery tissue is modeled with a computational probabilistic approach by assuming that the instantaneous stress at a specific strain varies according to normal distribution. Material parameters of the artery tissue which are modeled with a combined logarithmic and polynomial energy equation are represented by a statistical function with normal distribution. Mean and standard deviation of the material parameters are determined using genetic algorithm (GA) and inverse mean-value first-order second-moment (IMVFOSM) method, respectively. This nondeterministic approach was verified using computer simulation based on the Monte-Carlo (MC) method. Cumulative distribution function (CDF) of the MC simulation corresponds well with that of the experimental stress-strain data and the probabilistic approach is further validated using data from other studies. By taking into account the inhomogeneous mechanical properties of human biological tissue, the proposed method is suitable for realistic virtual simulation as well as an accurate computational approach for medical device validation. Probabilistic approach is used to model the inhomogeneity of human artery tissue.Tissue properties are represented by a statistical function with normal distribution.Mean value of the material parameters are identified using genetic algorithm.Empirical 3-sigma rule is used for reliability study of the statistical model.The statistical model represents the human artery properties accurately.
Year
DOI
Venue
2015
10.1016/j.compbiomed.2015.01.021
Computers in Biology and Medicine
Keywords
DocType
Volume
Tissue modeling,Probabilistic approach,Uncertainty analysis,Medical simulation,Human arterial tissue
Journal
59
Issue
ISSN
Citations 
C
1879-0534
1
PageRank 
References 
Authors
0.36
8
5
Name
Order
Citations
PageRank
Linfei Xiong171.17
Chee-Kong Chui224538.34
Yabo Fu310.36
Chee Leong Teo415923.42
Yao Li550.77