Analyzing Microstructural and Mechanical Properties of Porcine Tricuspid Valve Leaflets Through a Statistical Approach

Michael Barber, Chung-Hao Lee, Department of Aerospace and Mechanical Engineering, University of Oklahoma, 865 Asp. Ave., Felgar Hall, Felgar Hall 219C, Norman, OK 73019

The malfunction of the tricuspid valve (TV) leaflets causes poor blood flow through the right side of the heart, resulting in severe medical implications or death. In the case of tissue damage, tissue-engineered constructs, which are designed to have the same mechanical and microstructural characteristics as the native tissue, can replace the native valve. Therefore, the study of the mechanical and microstructural properties of native TV leaflets is of great importance to gain a better understanding of the microstructure-mechanics relationship and provide a basis for comparison of the constructed biomaterials. In this research we seek to understand TV tissue behavior by examining the microstructure and mechanical properties of biaxial tensile-tested TV leaflets through statistical histological analysis. After biaxially mechanically testing the TV leaflets, small sections of tissue are then dehydrated and stained, accenting target constituents with specific color. Next, high resolution pictures are taken of the tissue samples using a microscope and then examined in an in-house MATLAB program. The program converts the picture to a hue version of the image, which categorizes each colored pixel as different microstructural constituents based on the tissue stain. In the hue image, different tissue constituents are stained as: collagen (red-yellowish), glycosaminoglycan (green-blueish), cell (dark blue), and elastin (pink-reddish). The volume fraction of the individual constituents is obtained by finding the number of pixels of a constituent and dividing it by the total number of pixels. Furthermore, the probability density function of the individual constituents can be calculated using a normal distribution for each of the constituents. Using the volume fraction and normal distribution of each constituent, Bayesian statistics is further applied to find the posteriori probability, allowing for quantification of the probability of the constituent’s location and magnitude.

Additional Abstract Information

Presenter: Michael Barber

Institution: University of Oklahoma Norman Campus

Type: Poster

Subject: Engineering

Status: Approved

Time and Location

Session: Poster 6
Date/Time: Tue 2:00pm-3:00pm
Session Number: 4569