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Evaluation of bone fracture risk is important for the diagnosis and treatment of osteoporosis. Bone stiffness is a major factor in determining overall bone strength and fracture risk. With recent improvements in the spatial resolution of CT systems, it is possible to visualize bone microstructure and extract texture features. It is hypothesized that bone texture can be used to improve the assessment of bone strength compared to using bone mineral density (BMD) alone. In this work, we develop image analysis models for bone stiffness estimation utilizing deep learning (DL) features, radiomics features, and gradient structure tensors (GSTs) to estimate trabecular bone stiffness for high-resolution CT. We base our analysis on a dataset containing micro-CT images of 70 individual lumbar vertebrae. Ten trabecular bone ROIs were extracted from each vertebral body and their bone structure was segmented. The mechanical stiffness of each ROI was estimated using micro-finite element (μFE) analysis. Blur and correlated noise derived from clinical high-resolution CT systems were then added to the trabecular bone ROIs to generate simulated high-resolution CT images. A 3D residual network (ResNet) was trained to extract DL features to predict μFE-derived bone stiffness from the simulated CT images. Radiomics and GST features of bone ROIs were also computed for the same task. The prediction results for DL, radiomics, and GST features combined showed the best performance with a root mean square error (RMSE) of 2.646 N/μm and an R2 of 0.881. The performance of DL features alone was better than using BMD alone or using radiomic features alone. Additionally, incorporating orientation information from GST into the models resulted in improved accuracy. We demonstrate that μFEestimated mechanical properties of lumbar vertebral trabecular bone can be inferred from high-resolution CT images and that a combination of DL, radiomic, and GST features provides the highest prediction performance.
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