Computational Approaches for Segmenting Cartilage Morphology and Simulating Knee Joint Contact Pressure during Human Walking

Cartilage morphology is both an indicator of tissue health, and an important biomechanical determinant of internal joint mechanics. However, direct measurement of cartilage morphology and joint loading is not feasible. Thus, it is necessary to develop computational imaging and modeling tools to investigate the relationship between cartilage morphology and knee mechanics during human movement. Such tools are relevant clinically for tracking changes in morphology that can arise secondary to injury, surgical treatment and rehabilitation. Further, computational biomechanical modeling tools are beneficial in research for predicting the influence that interventions can have on joint loading patterns. The overall goal of this work was to develop, validate and use new computational approaches to accurately characterize in vivo cartilage morphology from MRI images and simulate tibiofemoral cartilage loading patterns during human walking. This goal was achieved by completing the following four objectives.;Objective 1. Develop an Accurate and Repeatable Semi-Automated Segmentation Algorithm for Reconstructing Articular Cartilage Morphology from Magnetic Resonance Image..;Objective 2. Introduce a Computationally Efficient Collision Detection Algorithm to enable the Calculation of Complex Cartilage Contact Pressure Patterns within Biomechanical Simulations of Movement..;Objective 3. Investigate the Accuracy of Simulated Tibiofemoral Contact Loads Obtained via the Co-Simulation of Neuromuscular Dynamics and Knee Mechanics..;Objective 4. Investigate the Influence of Cartilage Thickness on Simulated Tibiofemoral Contact Pressure Patterns during Normal Human Walking. .