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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. 

Abstract Osteoarthritis (OA), long considered a primary disorder of articular cartilage, is commonly associated with subchondral bone sclerosis. However, the cellular mechanisms responsible for changes to subchondral bone in OA, and the extent to which these changes are drivers of or a secondary reaction to cartilage degeneration, remain unclear. In knee joints from human patients with end-stage OA, we found evidence of profound defects in osteocyte function. Suppression of osteocyte perilacunar/canalicular remodeling (PLR) was most severe in the medial compartment of OA subchondral bone, with lower protease expression, diminished canalicular networks, and disorganized and hypermineralized extracellular matrix. As a step toward evaluating the causality of PLR suppression in OA, we ablated the PLR enzyme MMP13 in osteocytes while leaving chondrocytic MMP13 intact, using Cre recombinase driven by the 9.6-kb DMP1 promoter. Not only did osteocytic MMP13 deficiency suppress PLR in cortical and subchondral bone, but it also compromised cartilage. Even in the absence of injury, osteocytic MMP13 deficiency was sufficient to reduce cartilage proteoglycan content, change chondrocyte production of collagen II, aggrecan, and MMP13, and increase the incidence of cartilage lesions, consistent with early OA. Thus, in humans and mice, defects in PLR coincide with cartilage defects. Osteocyte-derived MMP13 emerges as a critical regulator of cartilage homeostasis, likely via its effects on PLR. Together, these findings implicate osteocytes in bone-cartilage crosstalk in the joint and suggest a causal role for suppressed perilacunar/canalicular remodeling in osteoarthritis.