Characterization of early stage cartilage degradation using diffuse reflectance near infrared spectroscopy

Visible and Near-Infrared Spectroscopy Enables Differentiation of Normal and Early Osteoarthritic Human Knee Joint Articular Cartilage

Osteoarthritis degenerates cartilage and impairs joint function. Early intervention opportunities are missed as current diagnostic methods are insensitive to early tissue degeneration. We investigated the capability of visible light-near-infrared spectroscopy (Vis-NIRS) to differentiate normal human cartilage from early osteoarthritic one. Vis-NIRS spectra, biomechanical properties and the state of osteoarthritis (OARSI grade) were quantified from osteochondral samples harvested from different anatomical sites of human cadaver knees. Two support vector machines (SVM) classifiers were developed based on the Vis-NIRS spectra and OARSI scores. The first classifier was designed to distinguish normal (OARSI: 0-1) from general osteoarthritic cartilage (OARSI: 2-5) to check the general suitability of the approach yielding an average accuracy of 75% (AUC = 0.77). Then, the second classifier was designed to distinguish normal from early osteoarthritic cartilage (OARSI: 2-3) yielding an average accuracy of 71% (AUC = 0.73). Important wavelength regions for differentiating normal from early osteoarthritic cartilage were related to collagen organization (wavelength region: 400-600 nm), collagen content (1000-1300 nm) and proteoglycan content (1600-1850 nm). The findings suggest that Vis-NIRS allows objective differentiation of normal and early osteoarthritic tissue, e.g., during arthroscopic repair surgeries.

In Situ Assessment of Porcine Osteochondral Repair Tissue in the Visible-Near Infrared Spectral Region

Standard assessment of cartilage repair progression by visual arthroscopy can be subjective and may result in suboptimal evaluation. Visible-near infrared (Vis-NIR) fiber optic spectroscopy of joint tissues, including articular cartilage and subchondral bone, provides an objective approach for quantitative assessment of tissue composition. Here, we applied this technique in the 350-2,500 nm spectral region to identify spectral markers of osteochondral tissue during repair with the overarching goal of developing a new approach to monitor repair of cartilage defects in vivo. Full thickness chondral defects were created in Yucatan minipigs using a 5-mm biopsy punch, and microfracture (MFx) was performed as a standard technique to facilitate repair. Tissues were evaluated at 1 month (in adult pigs) and 3 months (in juvenile pigs) post-surgery by spectroscopy and histology. After euthanasia, Vis-NIR spectra were collected in situ from the defect region. Additional spectroscopy experiments were carried out in vitro to aid in spectral interpretation. Osteochondral tissues were dissected from the joint and evaluated using the conventional International Cartilage Repair Society (ICRS) II histological scoring system, which showed lower scores for the 1-month than the 3-month repair tissues. In the visible spectral region, hemoglobin absorbances at 540 and 570 nm were significantly higher in spectra from 1-month repair tissue than 3-month repair tissue, indicating a reduction of blood in the more mature repair tissue. In the NIR region, we observed qualitative differences between the two groups in spectra taken from the defect, but differences did not reach significance. Furthermore, spectral data also indicated that the hydrated environment of the joint tissue may interfere with evaluation of tissue water absorbances in the NIR region. Together, these data provide support for further investigation of the visible spectral region for assessment of longitudinal repair of cartilage defects, which would enable assessment during routine arthroscopy, particularly in a hydrated environment.

Wavelength weightings in machine learning for ovine joint tissue differentiation using diffuse reflectance spectroscopy (DRS)

Objective: To investigate the DRS of ovine joint tissue to determine the optimal optical wavelengths for tissue differentiation and relate these wavelengths to the biomolecular composition of tissues. In this study, we combine machine learning with DRS for tissue classification and then look further at the weighting matrix of the classifier to further understand the key differentiating features. Methods: Supervised machine learning was used to analyse DRS data. After normalising the data, dimension reduction was achieved through multiclass Fisher's linear discriminant analysis (Multiclass FLDA) and classified with linear discriminant analysis (LDA). The classifier was first run with all the tissue types and the wavelength range 190 nm - 1081 nm. We analysed the weighting matrix of the classifier and then ran the classifier again, the first time using the ten highest weighted wavelengths and the second using only the single highest. Our method was applied to a dataset containing ovine joint tissue including cartilage, cortical and subchondral bone, fat, ligament, meniscus, and muscle. Results: It achieved a classification accuracy of 100% using the wavelength 190 nm - 1081 nm (2048 attributes) with an accuracy of 90% being present for 10 attributes with the exception of those with comparable compositions such as ligament and meniscus. An accuracy greater than 70% was achieved using a single wavelength, with the same exceptions. Conclusion: Multiclass FLDA combined with LDA is a viable technique for tissue identification from DRS data. The majority of differentiating features existed within the wavelength ranges 370-470 and 800-1010 nm. Focusing on key spectral regions means that a spectrometer with a narrower range can potentially be used, with less computational power needed for subsequent analysis.

Diffuse reflectance spectroscopy-enhanced drill for bone boundary detection

Intramedullary nailing is a routine orthopedic procedure used for treating fractures of femoral or tibial shafts. A critical part of this procedure involves the drilling of pilot holes in both ends of the bone for the placement of the screws that will secure the IM rod to sections of the fractured bone. This step introduces a risk of soft tissue damage because the drill bit, if not stopped in time, can transverse the bone-tissue boundary into the overlying muscle, causing unnecessary injury and prolonging healing time due to periosteum damage. In this respect, detecting the bone-tissue boundary before break-through can reduce the risks and complications associated with intramedullary nailing. Hence, in the present study, a two-wavelength diffuse reflectance spectroscopy technique was integrated into a surgical drill to optically detect bone-tissue boundary and automatically trigger the drill to stop. Furthermore, Monte-Carlo simulations were used to estimate the maximum distance from within the bone at which the bone-tissue boundary could be detected using DRS. The simulation results estimated that the detection distance, termed the "look-ahead-distance" was ∼1.5 mm for 1.3 mm source-detector fiber separation. Experimental measurements with 1.3 mm source-detector fiber separation showed that the look-ahead-distance was in the order of 250 µm in experiments with set drill rate and in the range of 1 mm in experiments where the holes were drilled by hand. Despite this difference, the automated DRS enhanced drill successfully detected the approaching bone tissue boundary when tested on samples of bovine femur and muscle tissue.

Near infrared spectroscopic imaging assessment of cartilage composition: Validation with mid infrared imaging spectroscopy

Disease or injury to articular cartilage results in loss of extracellular matrix components which can lead to the development of osteoarthritis (OA). To better understand the process of disease development, there is a need for evaluation of changes in cartilage composition without the requirement of extensive sample preparation. Near infrared (NIR) spectroscopy is a chemical investigative technique based on molecular vibrations that is increasingly used as an assessment tool for studying cartilage composition. However, the assignment of specific molecular vibrations to absorbance bands in the NIR spectrum of cartilage, which arise from overtones and combinations of primary absorbances in the mid infrared (MIR) spectral region, has been challenging. In contrast, MIR spectroscopic assessment of cartilage is well-established, with many studies validating the assignment of specific bands present in MIR spectra to specific molecular vibrations. In the current study, NIR imaging spectroscopic data were obtained for compositional analysis of tissues that served as an in vitro model of OA. MIR spectroscopic data obtained from the identical tissue regions were used as the gold-standard for collagen and proteoglycan (PG) content. MIR spectroscopy in transmittance mode typically requires a much shorter pathlength through the sample (≤10 microns thick) compared to NIR spectroscopy (millimeters). Thus, this study first addressed the linearity of small absorbance bands in the MIR region with increasing tissue thickness, suitable for obtaining a signal in both the MIR and NIR regions. It was found that the linearity of specific, small MIR absorbance bands attributable to the collagen and PG components of cartilage (at 1336 and 856 cm(-1), respectively) are maintained through a thickness of 60 μm, which was also suitable for NIR data collection. MIR and NIR spectral data were then collected from 60 μm thick samples of cartilage degraded with chondroitinase ABC as a model of OA. Partial least squares (PLS) regression using NIR spectra as input predicted the MIR-determined compositional parameters of PG/collagen within 6% of actual values. These results indicate that NIR spectral data can be used to assess molecular changes that occur with cartilage degradation, and further, the data provide a foundation for future clinical studies where NIR fiber optic probes can be used to assess the progression of cartilage degradation.

A constituent-based preprocessing approach for characterising cartilage using NIR absorbance measurements

Near-infrared spectroscopy is a widely adopted technique for characterising biological tissues. The high dimensionality of spectral data, however, presents a major challenge for analysis. Here, we present a second-derivative Beer’s law-based technique aimed at projecting spectral data onto a lower dimension feature space characterised by the constituents of the target tissue type. This is intended as a preprocessing step to provide a physically-based, low dimensionality input to predictive models. Testing the proposed technique on an experimental set of 145 bovine cartilage samples before and after enzymatic degradation, produced a clear visual separation between the normal and degraded groups. Reduced proteoglycan and collagen concentrations, and increased water concentrations were predicted by simple linear fitting following degradation (all \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} $p\ll 0.05$ \end{document}p≪0.05). Classification accuracy using the Mahalanobis distance was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} $\gt 98\%$ \end{document}>98% between these groups.

Nondestructive Assessment of Engineered Cartilage Composition by Near Infrared Spectroscopy

Tissue engineering presents a strategy to overcome the limitations of current tissue healing methods. Scaffolds, cells, external growth factors and mechanical input are combined in an effort to obtain constructs with properties that mimic native tissues. However, engineered constructs developed using similar culture environments can have very different matrix composition and biomechanical properties. Accordingly, a nondestructive technique to assess constructs during development such that appropriate compositional endpoints can be defined is desirable. Near infrared spectroscopy (NIRS) analysis is a modality being investigated to address the challenges associated with current evaluation techniques, which includes nondestructive compositional assessment. In the present study, cartilage tissue constructs were grown using chondrocytes seeded onto polyglycolic acid (PGA) scaffolds in similar environments in three separate tissue culture experiments and monitored using NIRS. Multivariate partial least squares (PLS) analysis models of NIR spectra were calculated and used to predict tissue composition, with biochemical assay information used as the reference data. Results showed that for combined data from all tissue culture experiments, PLS models were able to assess composition with significant correlations to reference values, including engineered cartilage water (at 5200 cm(-1), R = 0.68, p = 0.03), proteoglycan (at 4310 cm(-1), R = 0.82, p = 0.007), and collagen (at 4610 cm(-1), R = 0.84, p = 0.005). In addition, degradation of PGA was monitored using specific NIRS frequencies. These results demonstrate that NIR spectroscopy combined with multivariate analysis provides a nondestructive modality to assess engineered cartilage, which could provide information to determine the optimal time for tissue harvest for clinical applications.

Monitoring the Progression of Spontaneous Articular Cartilage Healing with Infrared Spectroscopy

OBJECTIVE

Evaluation of early compositional changes in healing articular cartilage is critical for understanding tissue repair and for therapeutic decision-making. Fourier transform infrared imaging spectroscopy (FT-IRIS) can be used to assess the molecular composition of harvested repair tissue. Furthermore, use of an infrared fiber-optic probe (IFOP) has the potential for translation to a clinical setting to provide molecular information in situ. In the current study, we determined the feasibility of IFOP assessment of cartilage repair tissue in a rabbit model, and assessed correlations with gold-standard histology.

DESIGN

Bilateral osteochondral defects were generated in mature white New Zealand rabbits, and IFOP data obtained from defect and adjacent regions at 2, 4, 6, 8, 12, and 16 weeks postsurgery. Tissues were assessed histologically using the modified O'Driscoll score, by FT-IRIS, and by partial least squares (PLS) modeling of IFOP spectra.

RESULTS

The FT-IRIS parameters of collagen content, proteoglycan content, and collagen index correlated significantly with modified O'Driscoll score (P = 0.05, 0.002, and 0.02, respectively), indicative of their sensitivity to tissue healing. Repair tissue IFOP spectra were distinguished from normal tissue IFOP spectra in all samples by PLS analysis. However, the PLS model for prediction of histological score had a high prediction error, which was attributed to the spectral information being acquired from the tissue surface only.

CONCLUSION

The strong correlations between FT-IRIS data and histological score support further development of the IFOP technique for clinical applications, although further studies to optimize data collection from the full sample depths are required.

Assessment of hyaline cartilage matrix composition using near infrared spectroscopy

Changes in the composition of the extracellular matrix (ECM) are characteristic of injury or disease in cartilage tissue. Various imaging modalities and biochemical techniques have been used to assess the changes in cartilage tissue but lack adequate sensitivity, or in the case of biochemical techniques, result in destruction of the sample. Fourier transform near infrared (FT-NIR) spectroscopy has shown promise for the study of cartilage composition. In the current study NIR spectroscopy was used to identify the contributions of individual components of cartilage in the NIR spectra by assessment of the major cartilage components, collagen and chondroitin sulfate, in pure component mixtures. The NIR spectra were obtained using homogenous pellets made by dilution with potassium bromide. A partial least squares (PLS) model was calculated to predict composition in bovine cartilage samples. Characteristic absorbance peaks between 4000 and 5000 cm(-1) could be attributed to components of cartilage, i.e. collagen and chondroitin sulfate. Prediction of the amount of collagen and chondroitin sulfate in tissues was possible within 8% (w/dw) of values obtained by gold standard biochemical assessment. These results support the use of NIR spectroscopy for in vitro and in vivo applications to assess matrix composition of cartilage tissues, especially when tissue destruction should be avoided.

Near-infrared spectroscopy correlates with established histological scores in a miniature pig model of cartilage regeneration

Near-Infrared Spectroscopy (NIRS) could be of clinical relevance in modern cartilage regeneration.In a miniature pig model correlation of measurements and histologic scores have never been used before. The data analysis was part of an animal project that investigated the effects of seeding a chondrogenic and osteogenic scaffold with a bone-marrow-derived cell concentrate and reports the histological and mechanical properties. We created 20 osteochondral defects in the femoral condyles of 10 miniature pigs.The defects were left empty (E), filled with the grafted cylinder upside down (U), or with a combined scaffold (S) containing a spongy bone cylinder covered with a collagen membrane. In the fourth group, the same scaffolds were implanted but seeded with a stem cell concentrate (S+BMCC). The animals were euthanized after 3 months, and histologic and spectrometric analyses were performed. NIRS measurements were significantly higher in the central area of the defects of group S+BMCC compared to the central area of the defects of group U. In all groups, a correlation between NIRS and the histologic scores could be demonstrated though on different levels. In the central area, a good NIRS measurement correlates with low (good) histologic scores. In group E and group S, this negative correlation was significant (p=0.01). For the first time, NIRS was successfully used to evaluate osteochondral constructs in a miniature pig model.

The contribution of bone and cartilage to the near-infrared spectrum of osteochondral tissue

Near-infrared (NIR) spectroscopy has been used to assess hyaline cartilage quality in human and animal osteochondral tissues. However, due to the lack of NIR signal from bone phosphate and the relatively deep penetration depth of the radiation, the separate contributions of cartilage and bone to the spectral signatures have not been well defined. The objectives of the current study were (1) to improve the understanding of the contributions of bone and cartilage to NIR spectra acquired from osteochondral tissue and (2) to assess the ability of this nondestructive method to predict cartilage thickness and modified Mankin grade of human tibial plateau articular cartilage. Near-infrared spectra were acquired from samples of bovine bone and cartilage with varying thicknesses and from 22 tibial plateaus harvested from patients undergoing knee replacement surgery. The spectra were recorded from regions of the tibial plateaus with varying degrees of degradation, and the cartilage thickness and modified Mankin grade of these regions were assessed histologically. The spectra from bone and cartilage samples of known thicknesses were investigated to identify spectral regions that were distinct for these two tissues. Univariate and multivariate linear regression methods were used to correlate modified Mankin grade and cartilage thickness with NIR spectral changes. The ratio of the NIR absorbances associated with water at 5270 and 7085 cm(-1) was the best differentiator of cartilage and bone spectra. The NIR prediction models for thickness and Mankin grade calculated using partial least squares regression were more accurate than were univariate-based prediction models, with a root mean square errors of cross-validation of 0.42 mm (for thickness) and 1.3 (for modified Mankin grade). We conclude that NIR spectroscopy may be used to simultaneously assess articular cartilage thickness and modified Mankin grade, based in part on differences in spectral contributions from bone and cartilage.

Near infrared spectroscopic evaluation of water in hyaline cartilage

In diseased conditions of cartilage such as osteoarthritis, there is typically an increase in water content from the average normal of 60-85% to greater than 90%. As cartilage has very little capability for self-repair, methods of early detection of degeneration are required, and assessment of water could prove to be a useful diagnostic method. Current assessment methods are either destructive, time consuming, or have limited sensitivity. Here, we investigated the hypotheses that non-destructive near infrared spectroscopy (NIRS) of articular cartilage can be used to differentiate between free and bound water, and to quantitatively assess water content. The absorbances centered at 5200 and 6890 cm(-1) were attributed to a combination of free and bound water, and to free water only, respectively. The integrated areas of both absorbance bands were found to correlate linearly with the absolute water content (R = 0.87 and 0.86) and with percent water content (R = 0.97 and 0.96) of the tissue. Partial least square models were also successfully developed and were used to predict water content, and percent free water. These data demonstrate that NIRS can be utilized to quantitatively determine water content in articular cartilage, and may aid in early detection of degenerative tissue changes in a laboratory setting, and with additional validations, possibly in a clinical setting.

Traumatic and degenerative cartilage lesions: arthroscopic differentiation using near-infrared spectroscopy (NIRS)

INTRODUCTION

Cartilage lesions or defects are the most common finding during knee arthroscopy. During arthroscopy, it is often difficult to differentiate between degenerative and traumatic cartilage lesions. The study aimed to determine the impact of near-infrared spectroscopy (NIRS) on the distinction between traumatic and degenerative cartilage lesions in the medial femoral condyle (MFC). It was hypothesized that NIRS as able to distinguish between traumatic and degenerative cartilage lesions.

MATERIALS AND METHODS

Arthroscopic evaluation was performed in six patients who had undergone anterior cruciate ligament (ACL) reconstruction and in six patients who had undergone high tibial osteotomy (HTO). In both groups, a grade III cartilage lesion was present within the MFC. NIRS evaluation was performed with a special probe (arthrospec-one, Arthrospec GmbH, Jena, Germany). NIRS measurements produced semi-quantitative values ranging from 0 (heavily degenerated cartilage) to 100 (completely intact cartilage).

RESULTS

The mean near-infrared-light absorption within the traumatic lesions in the MFC of the ACL group was 71.5 (range 61-80). In the HTO patients, this value was significantly (p < 0.001) lower at 31.7 (range 31-33). The margin of the MFC outside the lesion in the ACL group had the same adsorption as the lesion (p = 0.549).

CONCLUSION

After an injury, cartilage has a normal or nearly normal absorbance on near-infrared-light. Thus, it is possible to distinguish intraoperatively between traumatic and degenerative lesions. In addition, our results demonstrate that evaluating cartilage with NIRS is a dependable method for improving the diagnosis of significant chondral lesions.

A wireless handheld probe with spectrally constrained evolution strategies for diffuse optical imaging of tissue

We present a low-cost, portable, wireless diffuse optical imaging device. The handheld device is fast, portable, and can be applied to a wide range of both static and dynamic imaging applications including breast cancer, functional brain imaging, and peripheral artery disease. The continuous-wave probe has four near-infrared wavelengths and uses digital detection techniques to perform measurements at 2.3 Hz. Using a multispectral evolution algorithm for chromophore reconstruction, we can measure absolute oxygenated and deoxygenated hemoglobin concentration as well as scattering in tissue. Performance of the device is demonstrated using a series of liquid phantoms comprised of Intralipid(®), ink, and dye.