Generation of personalized finite element models of knee joint in a cohort of osteoarthritis patients

Ricci, Clara

Generation of personalized finite element models of knee joint in a cohort of osteoarthritis patients

Citation

Ricci, C. Generation of personalized finite element models of knee joint in a cohort of osteoarthritis patients. 2022. handle: http://hdl.handle.net/10230/54580

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Abstract

Osteoarthritis (OA) is a highly multifactorial disorder that largely lacks information for proper patient stratification. Its most prevalent form is knee OA. The core pathophysiology of the disorder consists in a dysregulation of articular chondrocyte activity. The later becomes catabolic and contributes to the destruction of the articular cartilage, which eventually generates chronic pain, stiffness, and loss of mobility. Mechanical loads are suspected to participate in the evolution of OA or at least in the evolution of the symptoms. Therefore, detailed descriptions of these loads in the knee joints might be useful to support the objective design of for both prevention and treatment strategies. Arguably, these loads cannot be measured in situ, but Finite Element (FE) models are valuable tools to create patient-specific models and simulate joint mechanics, at both the organ and tissue levels. Unfortunately, patient-specific modeling of knee joints remains an unmet challenge, mainly because of the complexity of the joint structure and shape, especially in patients affected by OA. Accordingly, the work consists in the generation and analysis of personalized FE models of the knee joints in a clinical cohort of 80 patients acquired in collaboration with the Rheumatology Service of the Hospital del Mar. An existing Bayesian Coherent Point Drift (BCPD) algorithm was applied to create a personalized 3D FE models of the knee joints of the patients, based on point set registration between a knee joint FE reference model that incorporates a structural mesh with all the tissues found in a healthy joint, and degenerated knee geometries reconstructed out of MRI. The quality of the mesh of the registered source, i.e., morphed, was determined, and used to define a multistep morphing pipeline and adjust the BCPD algorithm parameters.