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In bone-imaging research,in situsynchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of anin situSRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose. 

Abstract When studying bone fragility diseases, it is difficult to identify which factors reduce bone’s resistance to fracture because these diseases alter bone at many length scales. Here, we investigate the contribution of nanoscale collagen behavior on macroscale toughness and microscale toughening mechanisms using a bovine heat-treatment fragility model. This model is assessed by developing an in situ toughness testing technique for synchrotron radiation micro-computed tomography to study the evolution of microscale crack growth in 3D. Low-dose imaging is employed with deep learning to denoise images while maintaining bone’s innate mechanical properties. We show that collagen damage significantly reduces macroscale toughness and post-yield properties. We also find that bone samples with a compromised collagen network have reduced amounts of crack deflection, the main microscale mechanism of fracture resistance. This research demonstrates that collagen damage at the nanoscale adversely affects bone’s toughening mechanisms at the microscale and reduces the overall toughness of bone.