Identifying Spatial Variation in Soft Tissue Mechanics and Predicting Failure: Application to Cardiac Rupture

This award will support research that will increase our understanding of load-bearing soft tissues, promoting both the progress of science and advancing national health. Soft tissues such as tendons and blood vessels rely on their complex mechanical properties to withstand loads such as weight, force, or pressure. Unlike manmade materials, soft tissues can respond to changes in loading.

Cells can remodel their surroundings, altering the regional strength and stiffness of the tissue. Unfortunately, disease and injury can lead to remodeling that is insufficient to withstand continued loading, putting the tissue at risk of tearing and rupture. This award supports fundamental research needed to develop, test, and validate methods to quantify spatial variation and to predict tissue failure.

Quantifying spatial variation in material properties is essential since tissues fail at their weakest location. Without this capability, strategies to repair or replace injured tissue, tools to predict rupture risk, and therapies to reduce rupture propensity have all been stymied. Results from this research will benefit the U.S. health and society, impacting the fields of tissue mechanics, material science, surgery, and tissue engineering.

Within a soft tissue, damage or disease often causes cells to remodel their surrounding extracellular matrix, which dictates mechanical response, in a non-uniform manner. For example, a heart attack initiates a complex inflammatory response involving both the degradation and synthesis of matrix proteins, altering regional stiffness, strength, and anisotropy.

Though homogeneity is a common assumption, both local geometry and local material properties affect tissue failure. Therefore, measuring the spatial variation in material properties is essential for failure prediction. The work performed under this award will develop a nonlinear generalized inverse mechanics method to determine the heterogeneous properties of soft tissues and apply it to ventricular tissue after myocardial infarction, testing the hypothesis that the transition in mechanical properties is spatially more diffuse following late reperfusion therapy.

This work will also develop an algorithm to predict the location of failure and test the hypothesis that the behavior of reperfused and non-reperfused ventricular tissue are the same at sub-failure levels, but differ following tear initiation. The experimental techniques and analysis tools developed will be broadly applicable to many different tissue types and disease states.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.