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Abstract:
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Osteoarthritis is one of the most common disabling joint diseases. Magnetic resonance (MR) imaging has been commonly used to assess knee joint degeneration due to its distinct advantage in detecting morphologic cartilage changes. Although several statistical methods have been developed to perform quantitative cartilage analyses, little work has been done capturing the development of cartilage lesions (or abnormal regions) and how they naturally progress. The goal of this talk is to propose a dynamic abnormality detection and progression (DADP) framework for quantitative cartilage analysis, while addressing existing challenges. First, spatial correspondences are established on flattened 2D cartilage thickness maps extracted from 3D knee MR images both across time within each subject and across all subjects. Second, a dynamic functional mixed effects model is proposed to quantify abnormality progression across time points and subjects, while accounting for the spatio-temporal heterogeneity. We systematically evaluate our DADP using simulations and real data from the Osteoarthritis Initiative.
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