"BackgroundOsteoarthritis (OA) is one of the most prevalent disorders of the musculoskeletal system. In OA, articular cartilage degenerates and its structure and mechanical properties change, but monitoring or predicting the progression of OA has not been possible. Magnetic resonance imaging (MRI) is a potential tool for the imaging of joint tissues, estimating cartilage structure and diagnostics of OA, whereas joint loading and estimation of stresses/strains within joint tissues necessitates computational modeling. It would be a major breakthrough if one could develop a technique where, based on MRI and computational modeling, prediction and evaluation of OA progression of a patient under a certain loading condition would be possible.Objectives1) to combine MRI with computational modeling for the estimation of stresses and possible failure points within human knee joints, and 2) to develop second generation adaptive models of articular cartilage for the prediction of altered tissue structure and composition during OA progression. For the model validation, cartilage structure, composition and biomechanical properties as well as cell responses in situ are characterized. At the end of the project these main aims will be merged 3) to estimate the effect of loading on cartilage degeneration during the progression of OA in a patient-specific manner.SignificanceCombining MRI information of joint tissues with computational modeling, we develop a tool to evaluate the effect of different interventions on stresses in human joints. By combining this tool with an adaptive model that can estimate the effect of loading on cartilage composition and structure, we hope to be able to predict changes in cartilage properties during OA progression in a patient-specific manner several years ahead. This would help in decision making of clinical treatments and interventions (conservative or surgical) for the prevention or further progression of OA."