Conventional arthroscopic evaluation of articular cartilage is subjective and insufficient for assessing early compositional and structural changes during the progression of post-traumatic osteoarthritis. Therefore, in this study, arthroscopic near-infrared (NIR) spectroscopy is introduced, for the first time, for in vivo evaluation of articular cartilage thickness, proteoglycan (PG) content, and collagen orientation angle. NIR spectra were acquired in vivo and in vitro from equine cartilage adjacent to experimental cartilage repair sites. As reference, digital densitometry and polarized light microscopy were used to evaluate superficial and full-thickness PG content and collagen orientation angle. To relate NIR spectra and cartilage properties, ensemble neural networks, each with two different architectures, were trained and evaluated by using Spearman’s correlation analysis ( ρ ). The ensemble networks enabled accurate predictions for full-thickness reference properties (PG content: ρ in vitro, Val = 0.691, ρ in vivo = 0.676; collagen orientation angle: ρ in vitro, Val = 0.626, ρ in vivo = 0.574) from NIR spectral data. In addition, the networks enabled reliable prediction of PG content in superficial (25%) cartilage ( ρ in vitro, Val = 0.650, ρ in vivo = 0.613) and cartilage thickness ( ρ in vitro, Val = 0.797, ρ in vivo = 0.596). To conclude, NIR spectroscopy could enhance the detection of initial cartilage degeneration and thus enable demarcation of the boundary between healthy and compromised cartilage tissue during arthroscopic surgery.
PurposeWe investigated the feasibility of quantitative susceptibility mapping (QSM) for assessing degradation of articular cartilage by measuring ex vivo bovine cartilage samples subjected to different degradative treatments. Specimens were scanned at several orientations to study if degradation affects the susceptibility anisotropy. T2*‐mapping, histological stainings, and polarized light microscopy were used as reference methods. Additionally, simulations of susceptibility in layered geometry were performed.MethodsSamples (n = 9) were harvested from the patellae of skeletally mature bovines. Three specimens served as controls, and the rest were artificially degraded. MRI was performed at 9.4T using a 3D gradient echo sequence. QSM and T2* images and depth profiles through the centers of the samples were compared with each other and the histological findings. A planar isotropic model with depth‐wise susceptibility variation was used in the simulations.ResultsA strong diamagnetic contrast was seen in the deep and calcified layers of cartilage, while T2* maps reflected the typical trilaminar structure of the collagen network. Anisotropy of susceptibility in cartilage was observed and was found to differ from the T2* anisotropy. Slight changes were observed in QSM and T2* following the degradative treatments. In simulations, anisotropy was observed.ConclusionsThe results suggest that QSM is not sensitive to cartilage proteoglycan content, but shows sensitivity to the amount of calcification and to the integrity of the collagen network, providing potential for assessing osteoarthritis. The simulations suggested that the anisotropy of susceptibility might be partially explained by the layered geometry of susceptibility in cartilage.
Objective: To investigate the potential of quantitative susceptibility mapping (QSM) and T2* relaxation time mapping to determine mechanical and structural properties of articular cartilage via univariate and multivariate analysis. Methods: Samples were obtained from a cartilage repair study, in which surgically induced full-thickness chondral defects in the stifle joints of seven Shetland ponies caused post-traumatic osteoarthritis (14 samples). Control samples were collected from non-operated joints of three animals (6 samples). Magnetic resonance imaging (MRI) was performed at 9.4 T, using a 3-D multi-echo gradient echo sequence. Biomechanical testing, digital densitometry (DD) and polarized light microscopy (PLM) were utilized as reference methods. To compare MRI parameters with reference parameters (equilibrium and dynamic moduli, proteoglycan content, collagen fiber angle and-anisotropy), depth-wise profiles of MRI parameters were acquired at the biomechanical testing locations. Partial least squares regression (PLSR) and Spearman's rank correlation were utilized in data analysis. Results: PLSR indicated a moderate-to-strong correlation (r ¼ 0.49e0.66) and a moderate correlation (r ¼ 0.41e0.55) between the reference values and T2* relaxation time and QSM profiles, respectively (excluding superficial-only results). PLSR correlations were noticeably higher than direct correlations between bulk MRI and reference parameters. 3-D parametric surface maps revealed spatial variations in the MRI parameters between experimental and control groups. Conclusion: Quantitative parameters from 3-D multi-echo gradient echo MRI can be utilized to predict the properties of articular cartilage. With PLSR, especially the T2* relaxation time profile appeared to correlate with the properties of cartilage. Furthermore, the results suggest that degeneration affects the QSM-contrast in the cartilage. However, this change in contrast is not easy to quantify.
Chondral lesions lead to degenerative changes in the surrounding cartilage tissue, increasing the risk of developing post-traumatic osteoarthritis (PTOA). This study aimed to investigate the feasibility of quantitative magnetic resonance imaging (qMRI) for evaluation of articular cartilage in PTOA. Articular explants containing surgically induced and repaired chondral lesions were obtained from the stifle joints of seven Shetland ponies (14 samples). Three age-matched nonoperated ponies served as controls (six samples). The samples were imaged at 9.4 T. The measured qMRI parameters included T 1 , T 2 , continuous-wave T 1ρ (CWT 1ρ), adiabatic T 1ρ (AdT 1ρ), and T 2ρ (AdT 2ρ) and relaxation along a fictitious field (T RAFF). For reference, cartilage equilibrium and dynamic moduli, proteoglycan content and collagen fiber orientation were determined. Mean values and profiles from full-thickness cartilage regions of interest, at increasing distances from the lesions, were used to compare experimental against control and to correlate qMRI with the references. Significant alterations were detected by qMRI parameters, including prolonged T 1 , CWT 1ρ , and AdT 1ρ in the regions adjacent to the lesions. The changes were confirmed by the reference methods. CWT 1ρ was more strongly associated with the reference measurements and prolonged in the affected regions at lower spin-locking amplitudes. Moderate to strong correlations were found between all qMRI parameters and the reference parameters (ρ = −0.531 to −0.757). T 1 , low spin-lock amplitude CWT 1ρ , and AdT 1ρ were most responsive to changes in visually intact cartilage adjacent to the lesions. In the context of PTOA, these findings highlight the potential of T 1 , CWT 1ρ , and AdT 1ρ in evaluation of compositional and structural changes in cartilage.
The primary lesion arising from the initial insult after traumatic brain injury (TBI) triggers a cascade of secondary tissue damage, which may also progress to connected brain areas in the chronic phase. The aim of this study was, therefore, to investigate variations in the susceptibility distribution related to these secondary tissue changes in a rat model after severe lateral fluid percussion injury. We compared quantitative susceptibility mapping (QSM) and R 2 * measurements with histological analyses in white and grey matter areas outside the primary lesion but connected to the lesion site. We demonstrate that susceptibility variations in white and grey matter areas could be attributed to reduction in myelin, accumulation of iron and calcium, and gliosis. QSM showed quantitative changes attributed to secondary damage in areas located rostral to the lesion site that appeared normal in R 2 * maps. However, combination of QSM and R 2 * was informative in disentangling the underlying tissue changes such as iron accumulation, demyelination, or calcifications. Therefore, combining QSM with R 2 * measurement can provide a more detailed assessment of tissue changes and may pave the way for improved diagnosis of TBI, and several other complex neurodegenerative diseases.
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