The meniscus is a soft fibrous tissue that helps protect the joint surfaces in the knee. Meniscal tears account for over 500,000 hospital visits per year in the United States. The tearing of the meniscus and other soft tissues is not understood, making it difficult to predict when they will fail. Without understanding the microscopic events that cause failure of the individual tissue molecules, methods to prevent the failures are hard to create. This Faculty Early Career Development (CAREER) Program award will increase the scientific knowledge of failure in soft tissue when it is loaded either once or many times. This research includes experimental, theoretical and computer prediction approaches, including simulations of soft tissue fracture using advanced methods. This project will use the imaging and predictions developed from the research to create a computer program for high school educators that accurately simulates injury to the knee that can be used in science classes to motivate a student towards a scientific or medical career.
The central technical objective of this research project is to build and validate a computational model that describes and predicts failure in human meniscus. The research team will perform quasi-static and fatigue failure experiments on cadaveric specimens to determine whether meniscal fractures are regulated by collagen fiber alignment and density. Researchers will measure the organization and composition of the extracellular matrix in mechanically tested specimens to directly link soft tissue microstructure to failure behavior. The team will also develop mathematical models to predict fracture behavior based on the strain energy of the collagen fiber network, and will use these models to predict the experimental failures observed in young and aged meniscal tissue. This project will be the first to characterize the anisotropic fatigue behavior of human meniscus, and will be the first to visualize meniscal tears using an extended finite element method to split regions that exceed failure criteria.
