Data-driving based reconstruction for periarticular bone defect and inference and calibration of heterogeneous and anisotropic parameterized constitution
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Research and development of artificial bone repair materials and technology has become an urgent clinical demand to solve bone defects in orthopedics. A method based on mechanical modeling and data-driven is proposed for predicting the equivalent modulus of bone defect. A data-driven micro-CT and clinical-CT methods are proposed to identify the characteristics of bone trabeculae. The structural and mechanical properties of bone tissue are obtained through clinical-CT. A constitutive model of cancellous bone with 2 variables and 5 parameters is established to describe the characteristics of inhomogeneity and orthogonal anisotropy of bone tissue in different regions. Based on the experimental data of biological samples and the training and verification data of finite element simulation results, the model parameters are given by Bayesian inference and calibration. The digital triplet model is established for clinical-CT image in physical environment, equivalent modulus distribution in virtual environment and bone lattice design of 3D printed prosthesis. This technique has been verified by animal tibial reconstruction.
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