Finite element models of the knee can be used to identify regions at risk of mechanical failure in studies of osteoarthritis. Models of the knee often implement joint geometry obtained from magnetic resonance imaging (MRI) or gait kinematics from motion capture to increase model specificity for a given subject. However, differences exist in cartilage material properties regionally as well as between subjects. This paper presents a method to create subject-specific finite element models of the knee that assigns cartilage material properties from T 2 relaxometry.We compared our T 2 -refined model to identical models with homogeneous material properties. When tested on three subjects from the Osteoarthritis Initiative data set, we found the T 2 -refined models estimated higher principal stresses and shear strains in most cartilage regions and corresponded better to increases in KL grade in followups compared to their corresponding homogeneous material models. Measures of cumulative stress within regions of a T 2 -refined model also correlated better with the region's cartilage morphology MRI Osteoarthritis Knee Score as compared with the homogeneous model. We conclude that spatially heterogeneous T 2 -refined material properties improve the subject-specificity of finite element models compared to homogeneous material properties in osteoarthritis progression studies. Statement of Clinical Significance: T 2 -refined material properties can improve subject-specific finite element model assessments of cartilage degeneration.cartilage, finite element modeling, knee osteoarthritis, osteoarthritis initiative, T 2 relaxometry
| INTRODUCTIONOsteoarthritis (OA) of the knee is a painful and debilitating disease characterized by degeneration of articular cartilage. 1 Cartilage loss results from changes in the extracellular matrix, most notably the loss of proteoglycan and collagen content, which alters the tissue's mechanical properties. 2 An OA diagnosis can be confirmed with evidence of morphological changes, such as joint space narrowing and osteophytes, that are typically seen in X-ray imaging. However, significant cartilage degeneration can occur long before joint space narrowing or osteophytes are visible in plain X-ray images. 3 Several studies have even observed a poor correlation between cartilage degeneration and the radiographic features used to diagnose OA. [4][5][6][7] The insensitivity of radiography to identify early OA changes is a barrier to the timely implementation of nonoperative treatments.Modeling of OA is a growing field of research that aims to predict the
Simulating facial appearance change following bony movement is a critical step in orthognathic surgical planning for patients with jaw deformities. Conventional biomechanics-based methods such as the finite-element method (FEM) are labor intensive and computationally inefficient. Deep learning-based approaches can be promising alternatives due to their high computational efficiency and strong modeling capability. However, the existing deep learning-based method ignores the physical correspondence between facial soft tissue and bony segments and thus is significantly less accurate compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to estimate the facial appearance by transforming the bony movement to facial soft tissue through a point-to-point attentive correspondence matrix. Experimental results on patients with jaw deformity show that our proposed method can achieve comparable facial change prediction accuracy compared with the state-of-the-art FEMbased approach with significantly improved computational efficiency.
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