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We present a novel algorithm combined with computational modeling to simulate the development of knee osteoarthritis. The degeneration algorithm was based on excessive and cumulatively accumulated stresses within knee joint cartilage during physiological gait loading. In the algorithm, the collagen network stiffness of cartilage was reduced iteratively if excessive maximum principal stresses were observed. The developed algorithm was tested and validated against experimental baseline and 4-year follow-up Kellgren-Lawrence grades, indicating different levels of cartilage degeneration at the tibiofemoral contact region. Test groups consisted of normal weight and obese subjects with the same gender and similar age and height without osteoarthritic changes. The algorithm accurately simulated cartilage degeneration as compared to the Kellgren-Lawrence findings in the subject group with excess weight, while the healthy subject group’s joint remained intact. Furthermore, the developed algorithm followed the experimentally found trend of cartilage degeneration in the obese group (R2 = 0.95, p < 0.05; experiments vs. model), in which the rapid degeneration immediately after initiation of osteoarthritis (0–2 years, p < 0.001) was followed by a slow or negligible degeneration (2–4 years, p > 0.05). The proposed algorithm revealed a great potential to objectively simulate the progression of knee osteoarthritis.

Focal cartilage lesions can proceed to severe osteoarthritis or remain unaltered even for years. A method to identify high risk defects would be of utmost importance to guide clinical decision making and to identify the patients that are at the highest risk for the onset and progression of osteoarthritis. Based on cone beam computed tomography arthrography, we present a novel computational model for evaluating changes in local mechanical responses around cartilage defects. Our model, based on data obtained from a human knee in vivo, demonstrated that the most substantial alterations around the defect, as compared to the intact tissue, were observed in minimum principal (compressive) strains and shear strains. Both strain values experienced up to 3-fold increase, exceeding levels previously associated with chondrocyte apoptosis and failure of collagen crosslinks. Furthermore, defects at the central regions of medial tibial cartilage with direct cartilage-cartilage contact were the most vulnerable to loading. Also locations under the meniscus experienced substantially increased minimum principal strains. We suggest that during knee joint loading particularly minimum principal and shear strains are increased above tissue failure limits around cartilage defects which might lead to osteoarthritis. However, this increase in strains is highly location-specific on the joint surface.

Currently, there are no clinically available tools or applications which could predict osteoarthritis development. Some computational models have been presented to simulate cartilage degeneration, but they are not clinically feasible due to time required to build subject-specific knee models. Therefore, the objective of this study was to develop a template-based modeling method for rapid prediction of knee joint cartilage degeneration. Knee joint models for 21 subjects were constructed with two different template approaches (multiple templates and one template) based on the MRI data. Geometries were also generated by manual segmentation. Evaluated volumes of cartilage degeneration for each subject, as assessed with the degeneration algorithm, were compared with experimentally observed 4 year follow up Kellgren-Lawrence (KL) grades. Furthermore, the effect of meniscus was tested by generating models with subjectspecific meniscal supporting forces and those with the average meniscal supporting force from all models. All tested models were able to predict most severe cartilage degeneration to those subjects who had the highest KL grade after 4 year follow up. Surprisingly, in terms of statistical significance, the best result was obtained with one template approach and average meniscal support. This model was fully able to categorize all subjects to their experimentally defined groups (KL0, KL2 and KL3) based on the 4 year follow-up data. The results suggest that a template-or population-based approach, which is much faster than fully subject-specific, could be applied as a clinical prediction tool for osteoarthritis.

Organization of the collagen network is known to be different in healthy, osteoarthritic and repaired cartilage. The aim of the study was to investigate how the structure and properties of collagen network of cartilage modulate stresses in a knee joint with osteoarthritis or cartilage repair. Magnetic resonance imaging (MRI) at 1.5 T was conducted for a knee joint of a male subject. Articular cartilage and menisci in the knee joint were segmented, and a finite element mesh was constructed based on the two-dimensional section in sagittal projection. Then, the knee joint stresses were simulated under impact loads by implementing the structure and properties of healthy, osteoarthritic and repaired cartilage in the models. During the progression of osteoarthritis, characterized especially by the progressive increase in the collagen fibrillation from the superficial to the deeper layers, the stresses were reduced in the superficial zone of cartilage, while they were increased in and under menisci. Increased fibril network stiffness of repair tissue with randomly organized collagen fibril network reduced the peak stresses in the adjacent tissue and strains at the repair-adjacent cartilage interface. High collagen fibril strains were indicative of stress concentration areas in osteoarthritic and repaired cartilage. The collagen network orientation and stiffness controlled the stress distributions in healthy, osteoarthritic and repaired cartilage. The evaluation of articular cartilage function using clinical MRI and biomechanical modeling could enable noninvasive estimation of osteoarthritis progression and monitoring of cartilage repair. This study presents a step toward those goals.

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ABSTRACT:The purpose of the current study was to evaluate influences of radial tears and partial meniscectomy of lateral meniscus on the knee joint mechanics during normal walking by using computational modeling. A 3D geometry of a knee joint of a healthy patient was obtained from our previous study, whereas the data of normal walking were taken from the literature. Cartilage tissue was modeled as a fibril reinforced poroviscoelastic material, whereas meniscal tissue was modeled as a transverse isotropic elastic material. The realistic gait cycle data were implemented into the computational model and the effects of radial tears and partial meniscectemy of lateral meniscus on the knee joint mechanics were simulated. Middle, posterior, and anterior radial tears in lateral meniscus increased stresses by 300%, 430%, and 1530%, respectively, at the ends of tears compared to corresponding areas in the model with intact lateral meniscus. Meniscus tears did not alter stresses and strains at the tibial cartilage surface, whereas partial meniscectomy increased contact pressures, stresses, strains and pore pressures in the tibial cartilage by 50%, 44%, 21%, and 43%, respectively. Increased stresses and strains were observed primarily during the first $50% of the stance phase of the gait cycle. The present study suggests that anterior radial tear causes the highest risk for the development of total meniscal rupture, whereas partial meniscectomy increases the risk for the development of OA in lateral tibial cartilage. Highest risks for meniscus and cartilage failures are suggested to occur during the loading response and mid-stance of the gait cycle. In the future, the present modeling may be further developed to offer a clinical tool for aid in decision making of clinical interventions for patients with knee joint injuries. ß

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