Scaling of a full three-dimensional knee joint model for the study of osteoarthritis: towards developing a patient-specific model

Loayza Saldaña, Mayra Alejandra

Scaling of a full three-dimensional knee joint model for the study of osteoarthritis: towards developing a patient-specific model

Citation

Loayza Saldaña, M. Scaling of a full three-dimensional knee joint model for the study of osteoarthritis: towards developing a patient-specific model. 2021. handle: http://hdl.handle.net/10230/48222

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Abstract

Articular cartilage degeneration is consequence of knee osteoarthritis (OA). There is not a clear aetiology or understanding of OA’s natural course since it is a multifactorial condition. Finite Element models can be used to simulate knee joint mechanics and prevent injuries, being the geometry an essential part. The more accurate it is, the better will represent the tissues of the joint. However, most studies utilize a generic model of the knee which can lead to an over/under estimation of the effects of stimuli at the cartilages. Thus, the purpose of this study is to scale a 3D mesh of a knee joint to match the knee’s dimensions of actual patients with OA, as a first step to obtain a patient-specific model. For that, a segmented knee of a patient and the generic mesh of a knee model are used. The scaling is performed by implementing the homothetic function to the mesh. Then, gait simulations using real data of the force and time is conducted, by fixating the bottom of the tibia and applying the load on the rotation point of the knee model. Contact pressure (CP), water and proteoglycan content are evaluated. Results show a decrease in CP of 5.57% and an increase of 7.4% in the lateral and medial cartilage, respectively, of the scaled model compared to the non-scaled one. Zones with high CP present low water and proteoglycans content indicating a critical point of tissue degradation. Results suggest geometry must be considered to better interpret simulation’s outcomes. Moreover, the scaling method manages to adapt the dimensions of the model to the patient’s anatomy. Next step would be carrying out a morphing to obtain an actual representation of the knee geometry. With personalised data from simulations, clinicians can improve the diagnostic and develop more specific treatments for the patient.