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Utilizing a laser scanning confocal microscope system, the refractive indices of articular cartilage (AC) with mechanical or biochemical degenerations were characterized to investigate whether potential correlations exist between refractive index (RI) and cartilage degeneration. The cartilage samples collected from the medial femoral condyles of kangaroo knees were mechanically degenerated under different loading patterns or digested in trypsin solution with different concentrations. The sequences of RI were then measured from cartilage surface to deep region and the fluctuations of RI were quantified considering combined effects of fluctuating frequency and amplitude. The compositional and microstructural alterations of cartilage samples were assessed with histological methods. Along with the loss of proteoglycans, the average RI of cartilage increased and the local fluctuation of RI became stronger. Short-term high-speed test induced little influence to both the depth fluctuation and overall level of RI. Long-term low-speed test increased the fluctuation of RI but the average RI was barely changed. The results substantially demonstrate that RI of AC varies with both compositional and structural alterations and is potentially an indicator for the degeneration of AC. [ABSTRACT FROM AUTHOR]

Utilizing a laser scanning confocal microscope system, the refractive indices of articular cartilage (AC) with mechanical or biochemical degenerations were characterized to investigate whether potential correlations exist between refractive index (RI) and cartilage degeneration. The cartilage samples collected from the medial femoral condyles of kangaroo knees were mechanically degenerated under different loading patterns or digested in trypsin solution with different concentrations. The sequences of RI were then measured from cartilage surface to deep region and the fluctuations of RI were quantified considering combined effects of fluctuating frequency and amplitude. The compositional and microstructural alterations of cartilage samples were assessed with histological methods. Along with the loss of proteoglycans, the average RI of cartilage increased and the local fluctuation of RI became stronger. Short-term high-speed test induced little influence to both the depth fluctuation and overall level of RI. Long-term low-speed test increased the fluctuation of RI but the average RI was barely changed. The results substantially demonstrate that RI of AC varies with both compositional and structural alterations and is potentially an indicator for the degeneration of AC.

A close relationship has been found between the 3D collagen structure and physiological condition of articular cartilage (AC). Studying the 3D collagen network in AC offers a way to determine the condition of the cartilage. However, traditional qualitative studies are time consuming and subjective. This study aims to develop a computer vision-based classifier to automatically determine the condition of AC tissue based on the structural characteristics of the collagen network. Texture analysis was applied to quantitatively characterise the 3D collagen structure in normal (International Cartilage Repair Society, ICRS, grade 0), aged (ICRS grade 1) and osteoarthritic cartilages (ICRS grade 2). Principle component techniques and linear discriminant analysis were then used to classify the microstructural characteristics of the 3D collagen meshwork and the condition of the AC. The 3D collagen meshwork in the three physiological condition groups displayed distinctive characteristics. Texture analysis indicated a significant difference in the mean texture parameters of the 3D collagen network between groups. The principle component and linear discriminant analysis of the texture data allowed for the development of a classifier for identifying the physiological status of the AC with an expected prediction error of 4.23%. An automatic image analysis classifier has been developed to predict the physiological condition of AC (from ICRS grade 0 to 2) based on texture data from the 3D collagen network in the tissue.

Current musculoskeletal imaging techniques usually target the macro-morphology of articular cartilage or use histological analysis. These techniques are able to reveal advanced osteoarthritic changes in articular cartilage but fail to give detailed information to distinguish early osteoarthritis from healthy cartilage, and this necessitates high-resolution imaging techniques measuring cells and the extracellular matrix within the multilayer structure of articular cartilage. This review provides a comprehensive exploration of the cellular components and extracellular matrix of articular cartilage as well as high-resolution imaging techniques, including magnetic resonance image, electron microscopy, confocal laser scanning microscopy, second harmonic generation microscopy, and laser scanning confocal arthroscopy, in the measurement of multilayer ultra-structures of articular cartilage. This review also provides an overview for micro-structural analysis of the main components of normal or osteoarthritic cartilage and discusses the potential and challenges associated with developing non-invasive high-resolution imaging techniques for both research and clinical diagnosis of early to late osteoarthritis.