Calcified cartilage revealed in whole joint by X-ray phase contrast imaging

What is New in Osteoarthritis Imaging?

Osteoarthritis (OA) is the leading joint disorder globally, affecting a significant proportion of the population. Recent studies have changed our understanding of OA, viewing it as a complex pathology of the whole joint with a multifaceted etiology, encompassing genetic, biological, and biomechanical elements. This review highlights the role of imaging in diagnosing and monitoring OA. Today's role of radiography is discussed, while also elaborating on the advances in computed tomography and magnetic resonance imaging, discussing semiquantitative methods, quantitative morphologic and compositional techniques, and giving an outlook on the potential role of artificial intelligence in OA research.

In Situ Loading and Time-Resolved Synchrotron-Based Phase Contrast Tomography for the Mechanical Investigation of Connective Knee Tissues: A Proof-of-Concept Study

Articular cartilage and meniscus transfer and distribute mechanical loads in the knee joint. Degeneration of these connective tissues occurs during the progression of knee osteoarthritis, which affects their composition, microstructure, and mechanical properties. A deeper understanding of disease progression can be obtained by studying them simultaneously. Time-resolved synchrotron-based X-ray phase-contrast tomography (SR-PhC-µCT) allows to capture the tissue dynamics. This proof-of-concept study presents a rheometer setup for simultaneous in situ unconfined compression and SR-PhC-µCT of connective knee tissues. The microstructural response of bovine cartilage (n = 16) and meniscus (n = 4) samples under axial continuously increased strain, or two steps of 15% strain (stress-relaxation) is studied. The chondrocyte distribution in cartilage and the collagen fiber orientation in the meniscus are assessed. Variations in chondrocyte density reveal an increase in the top 40% of the sample during loading, compared to the lower half. Meniscus collagen fibers reorient perpendicular to the loading direction during compression and partially redisperse during relaxation. Radiation damage, image repeatability, and image quality assessments show little to no effects on the results. In conclusion, this approach is highly promising for future studies of human knee tissues to understand their microstructure, mechanical response, and progression in degenerative diseases.

Research on the morphological structure of partial fracture healing process in diabetic mice based on synchrotron radiation phase-contrast imaging computed tomography and deep learning

The prevalence of diabetes mellitus has exhibited a notable surge in recent years, thereby augmenting the susceptibility to fractures and impeding the process of fracture healing. The primary objective of this investigation is to employ synchrotron radiation phase-contrast imaging computed tomography (SR-PCI-CT) to examine the morphological and structural attributes of different types of callus in a murine model of diabetic partial fractures. Additionally, a deep learning image segmentation model was utilized to facilitate both qualitative and quantitative analysis of callus during various time intervals. A total of forty male Kunming mice, aged five weeks, were randomly allocated into two groups, each consisting of twenty mice, namely, simple fracture group (SF) and diabetic fracture group (DF). Mice in DF group were intraperitoneally injected 60 mg/kg 1 % streptozotocin(STZ) solution for 5 consecutive days, and the standard for modeling was that the fasting blood glucose level was ≥11.1 mmol /l one week after the last injection of STZ. The right tibias of all mice were observed to have oblique fractures that did not traverse the entire bone. At three, seven, ten and fourteen days after the fracture occurred, the fractured tibias were extracted for SR-PCI-CT imaging and histological analysis. Furthermore, a deep learning image segmentation model was devised to automatically detect, categorize and quantitatively examine different types of callus. Image J software was utilized to measure the grayscale values of different types of callus and perform quantitative analysis. The findings demonstrated that:1)SR-PCI-CT imaging effectively depicted the morphological attributes of different types of callus of fracture healing. The grayscale values of different types of callus were significantly different(P < 0.01).2)In comparison to the SF group, the DF group exhibited a significant reduction in the total amount of callus during the same period (P < 0.01). Additionally, the peak of cartilage callus in the hypertrophic phase was delayed.3)Histology provides the basis for training algorithms for deep learning image segmentation models. The deep-learning image segmentation models achieved accuracies of 0.69, 0.81 and 0.733 for reserve/proliferative cartilage, hypertrophic cartilage and mineralized cartilage, respectively, in the test set. The corresponding Dice values were 0.72, 0.83 and 0.76, respectively. In summary, SR-PCI-CT images are close to the histological level, and a variety of cartilage can be identified on synchrotron radiation CT images compared with histological examination, while artificial intelligence image segmentation model can realize automatic analysis and data generation through deep learning, and further determine the objectivity and accuracy of SR-PCI-CT in identifying various cartilage tissues. Therefore, this imaging technique combined with deep learning image segmentation model can effectively evaluate the effect of diabetes on the morphological and structural changes of callus during fracture healing in mice.

New imaging tools for mouse models of osteoarthritis

Osteoarthritis (OA) is a chronic degenerative disease characterized by a disruption of articular joint cartilage homeostasis. Mice are the most commonly used models to study OA. Despite recent reviews, there is still a lack of knowledge about the new development in imaging techniques. Two types of modalities are complementary: those that assess structural changes in joint tissues and those that assess metabolism and disease activity. Micro MRI is the most important imaging tool for OA research. Automated methodologies for assessing periarticular bone morphology with micro-CT have been developed allowing quantitative assessment of tibial surface that may be representative of the whole OA joint changes. Phase-contrast X-ray imaging provides in a single examination a high image precision with good differentiation between all anatomical elements of the knee joint (soft tissue and bone). Positron emission tomography, photoacoustic imaging, and fluorescence reflectance imaging provide molecular and functional data. To conclude, innovative imaging technologies could be an alternative to conventional histology with greater resolution and more efficiency in both morphological analysis and metabolism follow-up. There is a logic of permanent adjustment between innovations, 3R rule, and scientific perspectives. New imaging associated with artificial intelligence may add to human clinical practice allowing not only diagnosis but also prediction of disease progression to personalized medicine.