Abstract
Knee osteoarthritis (OA) is a painful joint disease, causing disabilities in daily activities. However, there is no known cure for OA, and the best treatment strategy might be prevention. Finite element (FE) modeling has demonstrated potential for evaluating personalized risks for the progression of OA. Current FE modeling approaches use primarily magnetic resonance imaging (MRI) to construct personalized knee joint models. However, MRI is expensive and has lower resolution than computed tomography (CT). In this study, we extend a previously presented atlas-based FE modeling framework for automatic model generation and simulation of knee joint tissue responses using contrast agent-free CT. In this method, based on certain anatomical dimensions measured from bone surfaces, an optimal template is selected and scaled to generate a personalized FE model. We compared the simulated tissue responses of the CT-based models with those of the MRI-based models. We show that the CT-based models are capable of producing similar tensile stresses, fibril strains, and fluid pressures of knee joint cartilage compared to those of the MRI-based models. This study provides a new methodology for the analysis of knee joint and cartilage mechanics based on measurement of bone dimensions from native CT scans.
Highlights
Osteoarthritis (OA) is the most common arthritic disease and is the leading cause of disability in the United States and other developed countries.[31,48] Knee OA, which prevalence has doubled since the mid-20th century, is the most prevalent form of OA.[49]
We presented a rapid atlas-based framework for generating finite element (FE) knee joint models with personalized cartilage volumes and topographies from contrast agent-free computed tomography (CT) images to simulate biomechanical responses of cartilage
This approach aims at addressing the lack of rapid and reliable methods in FE modeling of the knee joint and contributes to filling the gap between clinical use and high-fidelity FE models, striking a compromise between accuracy, availability, manual effort and computational complexity
Summary
Osteoarthritis (OA) is the most common arthritic disease and is the leading cause of disability in the United States and other developed countries.[31,48] Knee OA, which prevalence has doubled since the mid-20th century, is the most prevalent form of OA.[49]. Over the past two decades, computational finite element (FE) models have made remarkable advances in enabling a quantitative estimation of the local tissue stresses and deformations applied to the soft tissues of the knee joint during different loading conditions.[1,2,9,11,15,28,38,50] These biomechanical parameters have been utilized in predictive FE models to simulate personalized risks for the onset and progression of knee OA.[30,35,47] several obstacles need to be overcome prior to clinical use. These obstacles include the long time and high technical expertise required for generating FE models via manual image segmentation and meshing
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