Finite element modeling with subject‐specific mechanical properties to assess knee osteoarthritis initiation and progression

Abstract

Finite element models of the knee can be used to identify regions at risk of mechanical failure in studies of osteoarthritis. Models of the knee often implement joint geometry obtained from magnetic resonance imaging (MRI) or gait kinematics from motion capture to increase model specificity for a given subject. However, differences exist in cartilage material properties regionally as well as between subjects. This paper presents a method to create subject-specific finite element models of the knee that assigns cartilage material properties from T 2 relaxometry.We compared our T 2 -refined model to identical models with homogeneous material properties. When tested on three subjects from the Osteoarthritis Initiative data set, we found the T 2 -refined models estimated higher principal stresses and shear strains in most cartilage regions and corresponded better to increases in KL grade in followups compared to their corresponding homogeneous material models. Measures of cumulative stress within regions of a T 2 -refined model also correlated better with the region's cartilage morphology MRI Osteoarthritis Knee Score as compared with the homogeneous model. We conclude that spatially heterogeneous T 2 -refined material properties improve the subject-specificity of finite element models compared to homogeneous material properties in osteoarthritis progression studies. Statement of Clinical Significance: T 2 -refined material properties can improve subject-specific finite element model assessments of cartilage degeneration.cartilage, finite element modeling, knee osteoarthritis, osteoarthritis initiative, T 2 relaxometry | INTRODUCTIONOsteoarthritis (OA) of the knee is a painful and debilitating disease characterized by degeneration of articular cartilage. 1 Cartilage loss results from changes in the extracellular matrix, most notably the loss of proteoglycan and collagen content, which alters the tissue's mechanical properties. 2 An OA diagnosis can be confirmed with evidence of morphological changes, such as joint space narrowing and osteophytes, that are typically seen in X-ray imaging. However, significant cartilage degeneration can occur long before joint space narrowing or osteophytes are visible in plain X-ray images. 3 Several studies have even observed a poor correlation between cartilage degeneration and the radiographic features used to diagnose OA. [4][5][6][7] The insensitivity of radiography to identify early OA changes is a barrier to the timely implementation of nonoperative treatments.Modeling of OA is a growing field of research that aims to predict the

Towards a Transferable Modeling Method of the Knee to Distinguish Between Future Healthy Joints from Osteoarthritic Joints: Data from the Osteoarthritis Initiative

“…We assumed a scaled generic knee loading and a fixed flexion trajectory, limiting the study of the effects of subject-specific gait patterns that may overload or protect different contact regions in the knee. We did not use subject-specific material properties in the joint, which may show the site-specific onset of OA [ 24 ]. Another characteristic that we consider a limitation was not including local morphologies in the cartilage or subchondral bone that can induce the onset and progression of damage [ 37 ], making the scaling of the template geometry sound simplistic.…”

“…Modeling the biomechanics of living tissues in the human body is challenging due to the complex geometries and loads they bear. For the knee joint, several strategies based on finite element analysis (FEA) have been developed, accounting for the complex tissue structure, subject-specific geometries, and loading conditions of the joint [ 2 , 8 , 24 , 30 ]. In addition, when the studies involve cohort data, generating and running a large number ( N > 100) of personalized models are time-consuming processes [ 32 ].…”

X‐ray with finite element analysis is a viable alternative for MRI to predict knee osteoarthritis: Data from the Osteoarthritis Initiative

“…However, there are no clinically accepted methods for evaluating personalized risk for the onset and development of KOA. Therefore, researchers are currently developing novel methods, including computational modeling 6–8 or machine learning (ML)/deep learning (DL) 9,10 approaches, to answer this unmet need.…”

“…including computational modeling [6][7][8] or machine learning (ML)/deep learning (DL) 9,10 approaches, to answer this unmet need.…”