Abstract

Optical properties of biological tissues in the NIR spectral range have demonstrated significant potential for in vivo diagnostic applications and are critical parameters for modelling light interaction in biological tissues. This study aims to investigate the optical properties of articular cartilage as a function of tissue depth and integrity. The results suggest consistent wavelength-dependent variation in optical properties between cartilage depth-wise zones, as well as between healthy and degenerated tissue. Also, statistically significant differences (p<0.05) in both optical properties were observed between the different cartilage depth-wise zones and as a result of tissue degeneration. When taken into account, the outcome of this study could enable accurate modelling of light interaction in cartilage matrix and could provide useful diagnostic information on cartilage integrity.

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Articular cartilage is a specialized connective tissue covering the ends of bones in diarthrodial joints, such as the knee, hip, and shoulder. Its primary function is to provide frictionless articulation and transmission of physical loadings to the underlying subchondral bone without resulting in high stresses. Mature cartilage has limited repair capability due to a lack of vasculature and nerves. Thus, it is prone to degenerative changes resulting from age or injury. The extracellular matrix (ECM) of articular cartilage consists mainly of a collagen network (10–15%), proteoglycan (PG) macromolecules (5–10%) embedded within the collagen meshwork, and water (60–80%) [1]. Structurally, collagen fibers, being a major ECM component, are distributed with depth wise architecture with parallel orientation to the articular surface in SZ, random orientation in MZ and perpendicular orientation to the articular surface in DZ [2]. Likewise, concentration of PGs, being another major ECM component, shows significant increase from articular surface to the bone [2]. Furthermore, SZ has relatively higher number of flattened chondrocytes and higher water content [3]. Similarly, MZ has spherical shape chondrocytes with relatively low density as compared to SZ. While DZ has columnar clusters of chondrocytes [4] with highest PG content and lowest water content [3]. As the orientation of collagen fibers varies with depth from the articular surface to the subchondral bone, the structure of cartilage can be categorized into three distinct layers, namely, superficial zone (SZ) middle zone (MZ), and deep zone (DZ) [5]. SZ comprises approximately 0–10% of cartilage thickness [6] and collagen fibers in this zone are aligned parallel to the tissue surface. SZ is in direct contact with synovial fluid and protects the matrix from shear stresses. Adjacent to the SZ is the MZ, which spans 0–10% of the cartilage thickness and principally functions in shock absorption. Collagen fibers in MZ exhibit random orientation. The DZ covers about 70–90% of the cartilage matrix [6] and interfaces with the calcified cartilage. This zone provides the highest resistance to compressive forces and has collagen fibers oriented perpendicular to the cartilage-bone interface.

While PG macromolecules have been suggested to be strong absorbers of light and primary contributors to the absorption profile of articular cartilage [7] in the NIR spectral range, water is the main absorber of NIR light and influences the overall spectral profile of the tissue. However, absorption due to water are prominent at 1450 and 1900nm, where almost complete photon absorption can be observed. These absorptions in the NIR spectral range arise mainly from C-H, O-H, N-H and S-H bonds, which characterize the fundamental molecular make-up of biological tissues [8]. As light scattering can result from interaction with structural entities, it is likely that the primary light scattering agent in articular cartilage is its collagen fibril network and varying depth-wise orientation. Although chondrocytes may contribute to the light-scattering profile of articular cartilage; nevertheless, their contribution is likely to be minimal due to the limited cell population (2%) of the ECM [7].

Alteration of the composition and structure of articular cartilage are often linked to degenerative conditions, such as osteoarthritis (OA). OA is a multifactorial disease that results in the alteration of ECM, disruption of the collagen network, loss of PG content, increase in water content, and subsequent alteration in the functionality of the tissue. The global prevalence of OA is estimated to be up to 28% in adults more than 60 years old [9]. Surgical intervention for the treatment of joint pathologies is conducted via arthroscopy — a diagnostically subjective method with poor reliability in assessing injury severity and extent. The arthroscopic evaluation of articular cartilage is conducted by subjective visualization and palpation. Based on this evaluation, low-grade cartilage lesions are illustrated by superficial fissures, cracks, and softening of the cartilage. Similarly, high-grade cartilage lesions are identified as deep fissures through the MZ and DZ upto the subchondral bone or as a complete defect. The reliability of this subjective evaluation of cartilage integrity is strictly related to the experience of the concerned surgeon. Similarly, current clinical assessment of joint injuries and degeneration often involves clinical examination, followed by radiographic assessment, and if necessary, magnetic resonance imaging (MRI) is conducted to verify diagnosis for treatment planning. Subsequently, joint treatment or repair is conducted via arthroscopy. However, these methods have limitations that impact on the accuracy and reliability of diagnosis of joint pathologies. For example, radiographic assessment of cartilage degeneration is based on joint space narrowing [10], which is sensitive to advanced stages of tissue degeneration, while the resolution of clinical MRI poses a limitation on its application for assessment of early-stage degeneration [11]. More so, the limited availability of MRI [11] also poses a limitation on its use of characterizing joint defects. While arthroscopy is the gold standard for surgical intervention in the treatment of joint pathologies, the method is based on visual evaluation and manual palpation of the tissue to assess its integrity, and studies have shown that conventional arthroscopic evaluation is a highly subjective and poorly reproducible [12]. Thus, the present chain of protocols for assessment and treatment of joint pathologies is sub-optimal, particularly for tissues at the early stages of degeneration, where signs of degeneration are not immediately apparent or there are no visible cues. To address these limitations, several non-destructive and rapid optical methods, such as near infrared (NIR) spectroscopy, mid infrared (MIR) spectroscopy and optical coherence tomography (OCT), have been proposed. While MIR spectroscopy is sensitive to fundamental molecular vibrations, it has limited penetration depth into physiological tissues. OCT provides a means for high-resolution imaging of sub-surface tissue degeneration; however, its limited imaging depth (< 2 mm) [13] is a notable limitation, especially considering that human cartilage thickness varies between 1 and 6 mm. NIR spectroscopy bypasses these limitations by enabling rapid assessment of soft biological tissue in physiological conditions. With penetration depth of up to 10 mm into biological tissues [14], NIR spectroscopy enables assessment of full-thickness cartilage integrity, and potentially subchondral bone properties [15]. In addition, NIR spectroscopy requires no sample preparation, thus making it an ideal candidate for in vivo evaluation of articular cartilage integrity. Furthermore, in a recent study, NIR spectroscopy, in combination with OCT, was shown to be capable of comprehensive assessment of articular cartilage integrity during arthroscopy [16] along with estimation of structural integrity, composition and mechanical properties of articular cartilage using OCT signal [17]. Thus, this has led to research into more objective methods for reliable arthroscopic detection of alteration in the physiological properties of articular cartilage [18,19]. Near-infrared (NIR) spectroscopy, a spectroscopic technique, has shown significant potential for real-time characterization and diagnostic assessment of articular cartilage pathologies [18].

NIR spectroscopy has demonstrated excellent capacity for assessing the composition [20], structure, and integrity [21,22], physical [21,23], biomechanical [2325], ad biochemical [26] properties along of articular cartilage, including assessment [26] and monitoring [8] of cartilage degeneration. However, little is known on understanding the variation in optical properties along with the depth of the articular cartilage and based on tissue integrity. This study aims to investigate the correlation between the optical properties of articular cartilage and the depth (consistent with depth-wise variation in tissue structure and composition) and matrix integrity.

The optical properties (absorption coefficient: µa, and scattering coefficient: $\mu _s^{\prime}$) of biological tissues are key parameters that enable the description of light interaction in the tissue, potentially providing diagnostic information on the integrity of the tissue. Recent studies have shown that the optical properties of human skin vary with depth [27], providing useful knowledge for therapeutic and diagnostic purposes [28]. Detailed knowledge of the optical properties of biological tissues has enabled diagnostic applications including evaluation of blood oxygenation and tissue metabolism [29,30], imaging skin, and mucous cancer [31,32] and noninvasive skin cancer detection [33]. For example, chromophore concentrations based on the absorption spectra of skin play a crucial role in providing essential information for diagnostic applications including skin blood oxygenation, melanin concentration and detection of cancerous cells with fluorescence [29]. Furthermore, in the same study was also suggested that scattering power of human skin is dependent upon both anatomical location and skin phototype. Thus, extensive knowledge of tissue optical properties could provide important information for non-invasive assessment of skin for aesthetic and clinical applications. Likewise, variations in absorption and scattering coefficient of normal and cancerous skin have the capacity to provide a solid basis for noninvasive cancer detection [33]. Significant differences have been noticed primarily in scattering of normal and cancerous tissues in the spectral range 1050–1400 nm with cancerous lesions exhibiting lower scattering in comparison to that of healthy tissue. Thus, optical properties have enabled successful diagnostic application of cancerous tissue based on differences in both optical properties. Furthermore, optical properties are key parameters needed to accurately model the propagation of photons in biological tissue, providing critical knowledge such as the effect of the specific chromophore and thermal energy deposition.

In this study, we hypothesized that consistent with depth-wise variation in the structure and composition of articular cartilage, the optical properties (µa) and ($\mu _s^{\prime}$) of articular cartilage varies with the structure and composition, with these variations being depth-dependent and based on tissue integrity of articular cartilage. The scientific grounds for the presumed hypothesis are based on the varying orientation of the major constituents of the ECM matrix e.g., collagen fibres, water distribution, chondrocytes shape and density, and PG content along the articular cartilage depth. Thus, it is our position that this depth-dependent structure and composition of articular cartilage will result in different light interaction through the tissue depth, resulting in varying optical properties with tissue depth and integrity. To test this hypothesis, the optical properties of the different cartilage zones of visually normal and degenerated bovine patellar cartilage, as well as optical properties of healthy and degenerated full thickness cartilage were investigated.

2. Materials and methods

2.1 Sample preparation

Bovine knee joints, obtained from a local abattoir within one week of slaughter, were used in this study. Osteochondral samples (30×10×10 mm3 n=50) were extracted from the upper and lower medial facet (UMF, LMF), and the upper and lower lateral facet (ULF, LLF) of the patella (Fig. 1(a)), The samples were divided into three groups to enable measurement of depth-wise optical properties (A-specimens), bulk tissue optical properties (C-specimens), and biomechanical properties (B specimens) of articular cartilage. Cartilage thickness of A and B specimens was determined as the mean value of the thickness measured from the four edges of the specimen using an optical microscope (Zeiss, STEMI, SV8, Germany) [34].

 figure: Fig. 1.

Fig. 1. (a) Locations for sample extraction (b) Depth-wise cutting in layers for optical measurement.

Download Full Size | PPT Slide | PDF

Specimens A (depth-wise layers) was further processed for extraction of sections for measurement of optical properties of cartilage layers, while the B specimens were stored at −20 °C in a container filled with phosphate-buffered saline (PBS) for future biomechanical and histopathological assessment.

To extract tissue sections corresponding to the SZ, MZ, and DZ of articular cartilage, the A-specimens were subjected to a cryosection procedure immediately after thickness measurement. The bone-end of the specimens was first flattened to be parallel with the articular surface using a bandsaw. The bone-end was then mounted on a disc using a gel-like fixative medium (Optimal Cutting Temperature (OCT) compound, Thermo Fisher Scientific Ltd., Runcorn, UK). The assembly was kept in the chamber of a cryostat (Leica CM3050 S, Leica Biosystems, Wetzlar, Germany) for 5 minutes at −20 °C to allow rapid freezing and then it was placed on the rotary microtome within the cryostat chamber. The microtome allows slicing of sections with a thickness of 0.5–300 µm with a specimen precision orientation of 8°. Before sectioning, a disposable blade (MX35 Ultra, Thermo Fisher Scientific Ltd., Runcorn, UK) with the thickness of 0.24 mm and cutting angle of 34° was placed in the knife holder of the cryostat, and then the first 50 µm of the articular surface was cut and discarded to provide a uniformly smooth and flat surface for sectioning. A potential limitation of this study is related to removal of the first few tens of microns of tissue from the superficial zone due to the curvature of articular cartilage surface and the need to ensure flatness of the extracted sections. This is to avoid experimental error resulting from sample-related issues, such as air gap between the glass slide and cartilage section (resulting from non-flat sample). However, we believe that removal of this thin layer from SZ did not significantly affect the estimated optical properties of this zone as care was taken to ensure that the remaining tissue extracted for measurement were within the superficial layer. Subsequently, 10 consecutive tissue sections with a thickness of 150 or 250 µm were extracted and placed onto glass slides (thickness = 1 mm, Menzel-Gläser Frosted Microscope Slides, ThermoFisher Scientific Oy, Finland), hydrated with PBS, covered with a coverslip (thickness = 0.13 mm, Menzel Microscope Coverslips, ThermoFisher Scientific Oy, Finland) and then stored in a humid box at 4 °C for optical measurement. Determination of the different cartilage zones was based on collagen fibril orientation obtained from polarized light microscopy (PLM). Based on preliminary depth-wise collagen fibril orientation profiles, SZ, MZ and DZ were defined as the tissue depth where the collagen orientation was approximately 20 degrees, greater than 20 but less than 70 degrees, and greater than 70 degrees, respectively. This corresponded to 0–10% of tissue thickness for SZ, 10–30% for MZ, and 30–100% for DZ which is in accordance with the literature [6]. Thus, with an average thickness of 1.91 mm, the superficial zone of the samples used in this study is within the first 190 µm (less than 200 µm [35]) which sufficiently covers the depth where the SZ sections were extracted.

Cartilage layer from the C (bulk tissue) specimens was extracted by removing the subchondral bone using a bandsaw. The resulting cartilage layer was then sandwiched between a glass slide and coverslip for the optical measurement. The sandwiched samples were stored in similar conditions as the cartilage sections to minimize sample dehydration. Cartilage surface was regularly hydrated with PBS containing protease inhibitors [36] during the process of patella harvesting and sample preparation.

2.2 Optical measurement and determination of optical properties

To estimate the optical properties of cartilage zones and bulk tissue, diffuse reflectance (Rd) and transmittance (Td) measurements were conducted in the visible (400–600 nm) and NIR (600–2500 nm) spectral range using PerkinElmer Lambda 1050 spectrophotometer (PerkinElmer Inc., Waltham, USA) with a step size of 5 nm. The instrument is equipped with a single 150 mm integrating sphere for direct measurement of Rd and Td. The integrating sphere has transmittance and reflectance ports with diameters 20 mm and 25 mm, respectively. The spectrophotometer comprises of a photomultiplier tube detector (PMT, R6878) that spans the ultra-violet (UV) and visible spectral range, and a combined InGaAs and PbS detector which encompasses the NIR spectral range [7]. The spectrophotometer has deuterium and tungsten halogen light sources, and all optical measurements were carried out in a dark room to avoid external light contamination.

The Rd and Td measurements, sample thickness, and refractive index [37] were used to estimate the specimen's optical properties µa and $\mu _s^{\prime}$ using the inverse-adding (IAD) method [38]. In the IAD method, the total reflectance and transmittance for a single sample layer are calculated by solving the radiative transport equation [39]. The layer is then doubled, and the total reflectance and transmittance are calculated again. The IAD algorithm keeps estimating the optical properties through iteratively changing their values in the simulation until the IAD-simulated optical measurements match the measured ones [40,41]. Furthermore, as the IAD method incorporates the physical thickness of the samples for estimating their optical properties, it is notable that the variation in the thickness of cartilage sections would not affect the estimation of their optical properties.

Upon the estimation of the optical properties, the penetration depth (PD) [7] of the NIR photons into the bulk tissue of both healthy and degenerated cartilage was estimated using (Eq. 1):

$$PD = \; \frac{1}{{\sqrt {3{\mu _a}({{\mu_a} + {{\mu^{\prime}}_s}} )} }}$$

The optical properties in the spectral range (2000–2500 nm) were excluded in this study due to low signal to noise ratio of the NIR detector of the instrument and spectral saturation resulting from high water content in articular cartilage [42].

2.3 Instantaneous modulus and sample grouping

In order to investigate cartilage functional integrity, biomechanical testing was conducted on the B-specimens. Instantaneous modulus [43] of the samples was determined using a Mach-1 micromechanical testing system (V500css, Biomomentum Inc., Laval, Canada). The equipment comprises of a cylindrical indenter with flat face (diameter = 1 mm) and multi-axis force transducer (Nano17, ATI Industrial Automation Inc., Apex, USA). The small size of the indenter was chosen to reduce the impact of the subchondral bone on the measured stress [44]. Before measurement, the subchondral bone end of the samples was made parallel to the articular surface by filing and then glued to the bottom of the measurement flask, filled with PBS. 12.5 kPa pre-stress and 5 cycles of pre-conditioning loadings (strain amplitude = 2%) were applied to ensure perfect indenter-cartilage surface contact before measurement. The stress-relaxation protocol comprised of 3 steps with 5% strain amplitude and 100% strain rate, with a relaxation period of 15 minutes between steps [7]. To determine the instantaneous modulus, the ratio of the peak stress (at the 2nd step) and the strain amplitude was calculated. Cartilage was assumed to be elastic and isotropic during this protocol; thus, instantaneous modulus was calculated using solution described in [45] and by assuming Poisson’s ratio of 0.5 during instantaneous loading [46].

Based on the instantaneous modulus, the samples were divided into two groups using a threshold [47] of 4 MPa: healthy (n=22) and degenerated (n=28) group.

2.4 Histopathological evaluation

Articular cartilage integrity in the healthy and degenerated groups was further validated using a gold-standard histopathological evaluation method based on the Mankin scoring system [48]. The Mankin score is a combination of four major components assessing structural integrity (0–6 points), cellularity (0–3 points), matrix staining (0–4 points), and tidemark integrity (0–1 point). Tissue integrity is assessed via the 14-point scale, with 0 corresponding to perfectly healthy tissue and 14 corresponding to cartilage with severe degeneration. The Mankin score is evaluated on Safranin-O stained tissue sections, and the staining intensity is directly related tissue PG content [49]. In this study, the samples were scored by three independent observers twice with a week interval between scoring using the criteria laid down in [48].

2.5 Statistical analysis

Statistical analysis for the depth-dependent variations in optical properties within and between the different zones and bulk tissue of healthy and degenerated cartilage samples was performed using the student’s t-test and a one-way analysis of variance (ANOVA) test respectively. Normality test was also performed on the data set using Anderson-Darling goodness-of-fit hypothesis test (adtest). Based on the outcome of the normality test, non-parametric Wilcoxon rank sum test was used for non-normal dataset. A p-value of less than 0.05 was considered to indicate statistically significant differences. The reason for adopting traditional statistical approach (using p-values) instead of Principal Component Analysis (PCA) for comparison of optical properties (between the different depth-wise zones of normal and degenerated articular cartilage, as well as between both groups) was motivated by the need to perform the comparison on a wavelength-specific level, rather than a global comparison. Furthermore, PCA analysis would have dealt the datasets as a composite form and then decomposed into principal components, without performing the analysis at each individual wavelength of the NIR spectral range. All statistical analyses were conducted in MATLAB (MathWorks, Massachusetts, USA, version R2019b).

3. Results

Statistically significant difference was observed between samples in healthy and degenerated groups based on instantaneous modulus (p=0.0003) and Mankin scoring (p=0.002). The median (minimum, maximum, standard deviation) instantaneous modulus for healthy and degenerated groups are 4.71 MPa (4 MPa, 7.87 MPa, 1.29 MPa) and 3.27 MPa (1.36 MPa, 3.99 MPa, 0.69 MPa), respectively, which is well within the range reported in [47].

Similarly, the median (minimum, maximum, standard deviation) Mankin score for healthy and degenerated groups are 4.1 (3.4, 4.4, 0.8) and 5 (3.3, 5.8, 0.6) respectively. Consequently, a higher instantaneous modulus and lower Mankin for healthy cartilage, and a lower instantaneous modulus and higher Mankin for degenerated cartilage supports the segregation of the samples into healthy and degenerated groups, respectively [50,51].

3.1 Zonal variation in optical properties of healthy articular cartilage

The results show consistent wavelength-dependent variation in µa and $\mu _s^{\prime}$ across the different cartilage zones of healthy cartilage samples (Fig. 2 a, b), albeit with different magnitudes. This is largely consistent with the wavelength-dependent variation of optical properties in other biological tissues, such as skin, brain, breast, and fatty tissues [52]. In general, lower µa and higher $\mu _s^{\prime}$ were observed in the shorter NIR wavelength range (750–1000 nm) particularly in the 1st optical window [53] of 650–950 nm, where the penetration of light photons into soft tissue is highest.

 figure: Fig. 2.

Fig. 2. (a,b) Absorption and reduced scattering coefficients (µa and µ’s) of articular cartilage samples in healthy group along with standard error of mean (SEM).

Download Full Size | PPT Slide | PDF

Similarly, statistically significant difference (0.003 ≤ p ≤ 0.05 and 0.013≤ p ≤0.05) was observed for both µa and $\mu _s^{\prime}$ between SZ and MZ in the spectral ranges 600–1070 nm (Fig. 4(a)). and 600–950 nm (Fig. 4(b)) respectively. Furthermore, both the optical properties exhibit an increase in the spectral range 1350–2000 nm, most likely due to strong photon absorption by water, the main constituent (65–80%) of cartilage. The difference in µa and $\mu _s^{\prime}$ between the cartilage zones is noticeable particularly in $\mu _s^{\prime}$ (Fig. 2(b)). In healthy cartilage, statistically significant difference (0.034≤ p ≤0.05) in µa was found between SZ and DZ in the spectral range 1580–1855 nm (Fig. 4(a)).

 figure: Fig. 3.

Fig. 3. (a,b)$\; $Absorption and reduced scattering coefficients (µa and µ’s) of articular cartilage samples in degenerated group including SEM.

Download Full Size | PPT Slide | PDF

 figure: Fig. 4.

Fig. 4. (a,b) Statistical significance (t-test) for ${\mu _a}$ and $\mu _s^{\prime}$ between zones of articular cartilage samples in healthy group (c,d) Statistical significance (t-test) for ${\mu _a}$, and $\mu _s^{\prime}$ between zones of articular cartilage samples in degenerated group.

Download Full Size | PPT Slide | PDF

3.2 Zonal variation in optical characteristics of degenerated articular cartilage

Wavelength-dependent variation in µa and $\mu _s^{\prime}$ was observed across the different zones of degenerated cartilage samples (Figs. 3 a, b). In general, reduced µa and higher $\mu _s^{\prime}$ was observed in the shorter NIR wavelength range (750–1000 nm) with statistically significant difference in µa between SZ and MZ (0≤ p ≤0.05) in the spectral range 600–725 nm (Fig. 4(c)). Both optical properties exhibit an increase in the spectral range 1350–2000 nm with no statistically significant differences in µa between the zones in this spectral range. However, statistically significant difference was observed in $\mu _s^{\prime}$ between SZ and DZ (0.009≤ p ≤0.05, 0.0004≤ p ≤0.05) in the spectral ranges 1400–1560 nm and 1855–2000 nm respectively and between MZ and DZ (0.0067≤ p ≤0.05) in the spectral range 1900–1980 nm (Fig. 4(d)).

3.3 Depth-dependent differences in optical properties of healthy and degenerated articular cartilage

Statistically significant wavelength dependent difference was observed in µa and $\mu _s^{\prime}$ between the zones of healthy and degenerated articular cartilage samples. While the difference in µa and $\mu _s^{\prime}$ between the cartilage zones of both groups is noticeable, particularly in $\mu _s^{\prime}$ (Figs. 2(b), 3(b)), they were only statistically significant for µa in the shorter wavelength NIR spectral range (750–1000 nm). Statistically significant difference was noticed in µa between MZ of healthy and degenerated cartilage at discrete wavelengths of 1065 nm (p=0.03), 1135 nm (p=0.019) and 1150 nm (0.045) (Fig. 5(a)).

 figure: Fig. 5.

Fig. 5. (a) Statistical significance (t-test) for ${\mu _a}$ between the zones of articular cartilage samples in healthy and degenerated groups (b) Statistical significance (t-test) for $\mu _s^{\prime}$ between the zones of articular cartilage samples in healthy and degenerated groups.

Download Full Size | PPT Slide | PDF

In the spectral range 1300–1350 nm, statistically significant variation was only noticed in $\mu _s^{\prime}$ between SZ of healthy and degenerated cartilage at discrete wavelength 1340 nm (p=0.046) (Fig. 5(b)).

3.4 Differences in the optical properties of full thickness normal and degenerated articular cartilage

Similar trends observed in the cartilage zones (reduced µa and higher $\mu _s^{\prime}$) was also observed in the shorter NIR spectral range (750–1000 nm) in full thickness cartilage samples in healthy and degenerated groups (Fig. 6 a, b) with no statistically significant difference between the groups in µa and $\mu _s^{\prime}$ in this spectral range. Furthermore, increase in µa and $\mu _s^{\prime}$ were observed in the spectral range 1350–2000 nm between healthy and degenerated cartilage samples, most likely due to strong absorption of water, with potential trend but statistically insignificant differences in µa (p=0.072) and significant difference (0.015≤ p ≤0.041) in $\mu _s^{\prime}$ was observed in the spectral range 1445–1535 nm and at discrete wavelengths of 1880 nm (p=0.016) and 1970 nm (p=0.047) as shown in Fig. 6(c).

 figure: Fig. 6.

Fig. 6. (a) Absorption coefficient (µa) of full thickness normal and degenerated tissue (b) Reduced scattering coefficient ($\mu _s^{\prime}$) of full thickness normal and degenerated tissue (c) Statistical significance (t-test) for reduced scattering coefficient ($\mu _s^{\prime}$) between full thickness normal and degenerated tissue (d) Penetration depth of NIR photons into the healthy and degenerated cartilage

Download Full Size | PPT Slide | PDF

3.5 Differences in NIR light penetration depth in healthy and degenerated articular cartilage

The penetration of NIR photons into healthy and degenerated articular cartilage varies with wavelength (Fig. 6(d)). The penetration depth into healthy cartilage varies from 1.75 mm at 605 nm to 0.27 mm at 1985 nm with the highest penetration depth of 2.51 mm observed at 1085 nm. Similarly, the penetration depth in degenerated cartilage varies from 1.95 mm at 605 nm to 0.27 mm at 1985 nm with the maximum value of 2.48 nm at 1085 nm. Deep penetration depths of NIR photons in articular cartilage, noticed at relatively lower wavelengths (600–1050 nm), indicates weak absorption and strong scattering at these wavelengths. Similarly, decreasing trend in the penetration depth was noticed in the spectral range (1650–2000 nm) as shown in Fig.6d, where relatively higher absorption was noticed in both normal and degenerated articular cartilage (Fig. 6(a)). Furthermore, stronger absorption of NIR photons in degenerated cartilage was also apparent as lower penetration depth was observed compared to healthy cartilage at all wavelengths above 980 nm. Thus, as the wavelength increases, the masking effect of water absorption also increases [54], resulting in decreased penetration of photons into the tissue.

4. Discussion

Light propagation in biological tissues is influenced by tissue constituents [52] and depth-wise variation [55] in its structure and composition. The changes in optical properties observed are related to changes in composition and structure of articular cartilage both depth-wise and during degeneration. For example, the orientation of collagen fibrils in the SZ becomes less parallel to the articular surface in early stage degeneration when compared to the healthy tissue [56]. This is likely to alter the scattering profile of light as degeneration progresses. Similarly, decrease in PG content in the SZ with an accompanying increase in water content, which is one of the earliest signs of cartilage degeneration, is likely to result in elevation of NIR light absorption as can be observed in Fig. 6(a). Furthermore, these changes observed in the structure and composition of ECM also progressively impact on the biomechanical properties of articular cartilage due to structure-function relationship of the tissue. Similarly, monitoring changes in the optical properties could provide a means for nondestructively tracking alteration in cartilage functional properties. [22]. Thus, tissue optical properties, which are functions of its structure and composition, are expected to vary with depth. This depth-wise variation in structure and composition is arguably the reason for the differences in cartilage optical properties (µa and $\mu _s^{\prime}$) observed along the tissue depth in healthy and degenerated articular cartilage (Fig. 2, Fig. 3). Specifically, the difference in µa observed between SZ and MZ and SZ and DZ in both groups is likely due to differences in the depth-wise composition (PG, collagen and water content) of cartilage [56]. In addition, the difference observed in $\mu _s^{\prime}$ is likely due to depth-wise structural variation in the tissue, which is related to the collagen fibril orientation, and to a lesser extent, the chondrocyte population in the tissue.

During degeneration, articular cartilage experiences alteration in its ECM, including loss of superficial PG content at the early stages, and extending to disruption of the collagen framework of the tissue at more advanced stages. Thus, the difference in optical properties between the healthy and degenerated cartilage can be attributed to degenerative changes in the tissue’s structure and composition. For example, the statistically significant difference in µa observed in the region of 1445–1535 nm is likely due to the increase in tissue water content that has been shown to accompany cartilage degeneration [57]. Furthermore, the scattering profile of more organised collagen fibers is expected to be different from that of a disorganised collagen meshwork in degenerated tissue. Thus, the difference in $\mu _s^{\prime}$ observed in the same region is arguably due to differences in collagen fiber organisation, which is a major biomarker of cartilage tissue integrity. The increased absorption of light (Fig. 6(a)) due to increased water content, and reduced scattering (Fig. 6(b)) results in reduced light penetration in degenerated compared to normal articular cartilage (Fig. 6(d)).

The findings of this study provide critical knowledge for better understanding of light-tissue interaction via modelling. A computational approach for modelling light-tissue interaction has been developed in [58] for multi-layered biological tissues. This approach highlighted the importance of layer-specific optical properties for accurate modelling of light interaction in heterogeneous materials, such as biological tissues. Thus, the present study highlights the importance of investigating variation in optical properties of cartilage with depth and tissue integrity. For example, modelling light-tissue interaction in articular cartilage using the layer-specific optical properties is likely to yield a different and more accurate result (e.g., fluence map) compared to when the modelling is conducted using optical properties of the bulk tissue, which assumes the tissue to be a homogenous material.

The variation in optical properties with tissue depth and integrity also provides useful information for application of optical properties for tissue characterization and diagnostic purposes. For example, a recent study [8] demonstrated the potential of NIR spectroscopy for estimation of proteoglycan loss in cartilage, albeit without depth-specific information. The outcome of the present study, i.e., knowledge of depth-wise variation in cartilage optical properties could enable depth-resolved cartilage evaluation, including accurate subchondral bone assessment. NIR spectra has proved to be responsive to both micro and macroscopic properties of articular cartilage [23]. We believe that alteration of articular cartilage composition and structure can be detected from variation in its optical properties across the depth of the cartilage. Similarly, depth-dependent changes resulting from tissue degeneration, especially in early-stages where cartilage degeneration are more subtle, can be observed through the assessment of depth-specific optical properties. Thus, in the context of the current study, identification of wavelengths across the NIR spectral range where differences in depth-specific optical properties are maximized holds significant diagnostic implications. For example, this could extend an approach for adapting diffuse reflectance NIR spectroscopy for in vivo diagnostic assessment of specific cartilage depths during arthroscopic surgery [36]. As degenerated articular cartilage is subject to alterations in tissue composition and structure, thus the approach adopted in the current study can be a handy tool in detecting early stages of erosion in articular cartilage.

Furthermore, similar to a structure-function relationship, optical properties of biological tissues are intrinsically related to their structure and composition and are thus potential biomarkers of its integrity. Also, they can serve as indicators to monitor changes in the cartilage matrix, e.g., during growth in a bioreactor. The literature in term of an instrument to measure both optical properties in vivo is limited. The primary reason being that investigating in vivo living tissue is subject to variations in its composition e.g. collagen content, water presence, PG content and chondrocytes. However, as this study highlighted that alteration of tissue structure and composition resulting from degeneration can be observed from the varying optical properties across the tissue depth, it could potentially lead towards improving or optimizing the existing NIR arthroscopic tool for diagnostic assessment of cartilage integrity. Another key finding of the present study was that the wavelength-dependent penetration of light is also affected by tissue integrity, as observed for degenerated cartilage, where penetration depth was less than in healthy cartilage, particularly at wavelengths above 980 nm. Furthermore, reduced light penetration in degenerated articular cartilage in comparison to normal articular cartilage has also been highlighted in terms of optical attenuation index (OAI) [59], which is a measure of signal propagation and determined as the tissue depth with 15% decrease of the average signal intensity at the surface of the tissue surface [59]. The same study showed that degeneration-related reduction in imaging depth was observed in OCT images with decreasing OAI values, thus the observation of lower optical attenuation with higher penetration depth due to less absorption (Fig. 6(a)) and more scattering (Fig. 6(b)) in normal articular cartilage is in accordance with the findings of this study. Furthermore, less penetration depth (higher optical attenuation) observed in degenerated articular cartilage can be speculated to be triggered by the loss PG content and increase in water content [59].

In this study, bovine samples were used to evaluate the variation in optical properties of articular cartilage with depth and tissue integrity. Statistically significant differences were observed between the zones of healthy and degenerated articular cartilage, consistent with depth-wise variation in tissue structure and composition. The main limitation of the present study is that bovine cartilage with limited number of samples (n=50) was used. Higher number of samples extracted from human joints would provide more insight into the effect of depth-wise variation and tissue integrity on cartilage optical properties. This would enable validation of the findings presented in this study. In addition, another limitation was the removal of the first few tens of microns, potentially corresponding to a thin layer from SZ of the articular cartilage. We believe, this did not significantly affect the estimated optical properties of SZ in the case of normal articular cartilage samples; however, this may not be the case for degenerated cartilage samples, as this may results in loss of some useful details. More so, validation with human samples would be required before the findings of the present study can be translated to potential clinical application.

5. Conclusion

In conclusion, optical properties of articular cartilage varies with depth and tissue integrity. This finding is consistent with depth-wise and degeneration related variation in tissue structure and composition. This could enable better understanding of light-tissue interaction via modelling, in the NIR spectral range with potential for optimising the application of optical techniques, such as NIR spectroscopy in biological tissue diagnostic applications.

Funding

Academy of Finland (Projects (315820, 320135)); Horizon 2020 Framework Programme (Grant Agreement No 780598, H2020-ICT-2017-1).

Acknowledgments

Dr. Afara acknowledges funding from the Academy of Finland (Projects 315820, 320135). Dr. Nippolainen, and Dr. Shaikh acknowledge funding from Europe Union’s Horizon 2020 research and innovation programme (H2020-ICT-2017-1), Grant Agreement No 780598.

Disclosures

The authors declare no conflict of interest.

Data availability

The concerned datasets and codes regarding results presented in this paper can be provided upon request from the authors.

Supplemental document

See Supplement 1 for supporting content.

References

1. J.A. Buckwalter and H.J. Mankin, “Articular cartilage: Part I,” J. Bone Jt. Surg. 79(4), 600 (1997). [CrossRef]  

2. A. J. Sophia Fox, A. Bedi, and S.A. Rodeo, “The basic science of articular cartilage: structure, composition, and function,” Sports Health 1(6), 461–468 (2009). [CrossRef]  

3. E. Jung, H. Choi, M. Lim, H. Kang, H. Park, H. Han, B. Min, S. Kim, I. Park, and H. Lim, “Quantitative analysis of water distribution in human articular cartilage using terahertz time-domain spectroscopy,” Biomed. Opt. Express 3(5), 1110–1115 (2012). [CrossRef]  

4. M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010). [CrossRef]  

5. H. Muir, “The chondrocyte, architect of cartilage. Biomechanics, structure, function and molecular biology of cartilage matrix macromolecules,” BioEssays 17(12), 1039–1048 (1995). [CrossRef]  

6. T. J. Klein, J. Malda, R. L. Sah, and D. W. Hutmacher, “Tissue engineering of articular cartilage with biomimetic zones,” Tissue Eng., Part B 15(2), 143–157 (2009). [CrossRef]  

7. I. Kafian-Attari, E. Nippolainen, D. Semenov, M. Hauta-Kasari, J. Töyräs, and I. O. Afara, “Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity,” Biomed. Opt. Express 11(11), 6480–6494 (2020). [CrossRef]  

8. I.O. Afara, I. Prasadam, Z. Arabshahi, Y. Xiao, and A. Oloyede, “Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy,” Sci. Rep. 7(1), 11463–9 (2017). [CrossRef]  

9. WHO, Chronic Rheumatic Conditions. (2014).

10. J. C. Buckland-Wright, “Quantitative radiography of osteoarthritis,” Ann. Rheum. Dis. 53(4), 268–275 (1994). [CrossRef]  

11. L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010). [CrossRef]  

12. G. Spahn, H.M. Klinger, and G.O. Hofmann, “How valid is the arthroscopic diagnosis of cartilage lesions? Results of an opinion survey among highly experienced arthroscopic surgeons,” Arch Orthop Trauma Surg 129(8), 1117–1121 (2009). [CrossRef]  

13. N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013). [CrossRef]  

14. F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991). [CrossRef]  

15. I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models,” Bone 53(2), 350–357 (2013). [CrossRef]  

16. J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017). [CrossRef]  

17. P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015). [CrossRef]  

18. G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007). [CrossRef]  

19. I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021). [CrossRef]  

20. U. P Palukuru, C. M. McGoverin, and N. Pleshko, “Assessment of hyaline cartilage matrix composition using near infrared spectroscopy,” Matrix Biol. 38, 3–11 (2014). [CrossRef]  

21. I. O. Afara, S. Singh, and A. Oloyede, “Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage,” Medical Engineering Physics 35(1), 88–95 (2013). [CrossRef]  

22. M. Maldonado and J. Nam, “The role of changes in extracellular matrix of cartilage in the presence of inflammation on the pathology of osteoarthritis,” Biomed Research International, 2013.

23. I. O. Afara, M. Hauta-Kasari, J. S. Jurvelin, A. Oloyede, and J. Töyräs, “Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition,” Physiol. Meas. 36(9), 1913–1928 (2015). [CrossRef]  

24. G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008). [CrossRef]  

25. I. O. Afara, S. Singh, and A. Oloyede, “Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra,” Journal of the mechanical behaviour of biomedical material 20, 249–258 (2013). [CrossRef]  

26. I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score,” Osteoarthritis and cartilage 20(11), 1367–1373 (2012). [CrossRef]  

27. L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995). [CrossRef]  

28. T. Lister, P. Wright, and P. Chappell, “Spectrophotometers for the clinical assessment of port-wine stain skin lesions: a review,” Lasers Med. Sci. 25(3), 449–457 (2010). [CrossRef]  

29. S. H. Tseng, P. Bargo, A. Durkin, and N. Kollias, “Chromophore concentrations, absorption and scattering properties of human skin in-vivo,” Opt. Express 17(17), 14599–14617 (2009). [CrossRef]  

30. J. Huang, S. Zhang, S. Gnyawali, C. K. Sen, and R. X. Xu, “Second derivative multispectral algorithm for quantitative assessment of cutaneous tissue oxygenation,” J. Biomed. Opt. 20(3), 036001 (2015). [CrossRef]  

31. S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017). [CrossRef]  

32. X. Chen, W. Lin, C. Wang, S. Chen, J. Sheng, B. Zeng, and M. Xu, “In vivo real-time imaging of cutaneous hemoglobin concentration, oxygen saturation, scattering properties, melanin content, and epidermal thickness with visible spatially modulated light,” Biomed. Opt. Express 8(12), 5468–5482 (2017). [CrossRef]  

33. E.V. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11(6), 064026 (2006). [CrossRef]  

34. J. S. Jurvelin, T. Räsänen, P. Kolmonens, and T. Lyyra, “Comparison of optical, needle probe and ultrasonic techniques for the measurement of articular cartilage thickness,” Journal of biomechanics 28(2), 231–235 (1995). [CrossRef]  

35. X. Zhou, M. J. Ju, L. Huang, and S. Tang, “Slope-based segmentation of articular cartilage using polarization-sensitive optical coherence tomography phase retardation image,” J. Biomed. Opt. 24(03), 1 (2019). [CrossRef]  

36. M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019). [CrossRef]  

37. S. Z. Wang, Y. P. Huang, Q. Wang, Y. P. Zheng, and Y. H. He, “Assessment of depth and degeneration dependences of articular cartilage refractive index using optical coherence tomography in vitro,” Connect. Tissue Res. 51(1), 36–47 (2010). [CrossRef]  

38. H. C. Van de Hulst, Multiple Light Scattering: Tables, Formulas, and Applications (Elsevier, 2012).

39. S. Chandrasekhar, Radiative Transfer (Dover Publications, 2013).

40. S. A. Prahl, M. J. van Gemert, and A. J. Welch, “Determining the optical properties of turbid media by using the adding–doubling method,” Appl. Opt. 32(4), 559–568 (1993). [CrossRef]  

41. W. Saeys, M. A. Velazco-Roa, S. N. Thennadil, H. Ramon, and B. M. Nicolaï, “Optical properties of apple skin and flesh in the wavelength range from 350 to 2200 nm,” Appl. Opt. 47(7), 908–919 (2008). [CrossRef]  

42. I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020). [CrossRef]  

43. A. F. Mak, W. M. Lai, and V. C. Mow, “Biphasic indentation of articular cartilage—I. Theoretical analysis,” Journal of biomechanics 20(7), 703–714 (1987). [CrossRef]  

44. A. Thambyah, A. Nather, and J. Goh, “Mechanical properties of articular cartilage covered by the meniscus,” Osteoarthritis and Cartilage 14(6), 580–588 (2006). [CrossRef]  

45. W. C. Hayes, L. M. Keer, G. Herrmann, and L. F. Mockros, “A mathematical analysis for indentation tests of articular cartilage,” Journal of biomechanics 5(5), 541–551 (1972). [CrossRef]  

46. J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014). [CrossRef]  

47. M. Prakash, J. K. Sarin, L. Rieppo, I. O. Afara, and J. Töyräs, “Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage,” Appl. Spectrosc. 71(10), 2253–2262 (2017). [CrossRef]  

48. H. J. Mankin, M. E. Johnson, and L. Lippiello, “Biochemical and metabolic abnormalities in articular cartilage from osteoarthritic human hips. III. Distribution and metabolism of amino sugar-containing macromolecules,” JBJS 63(1), 131–139 (1981). [CrossRef]  

49. I. Kiviranta, J. Jurvelin, A. M. Säämänen, and H. J. Helminen, “Microspectrophotometric quantitation of glycosaminoglycans in articular cartilage sections stained with Safranin O,” Histochemistry 82(3), 249–255 (1985). [CrossRef]  

50. R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018). [CrossRef]  

51. R. U. Kleemann, D. Krocker, A. Cedraro, J. Tuischer, and G. N. Duda, “Altered cartilage mechanics and histology in knee osteoarthritis: relation to clinical assessment (ICRS Grade),” Osteoarthritis and cartilage 13(11), 958–963 (2005). [CrossRef]  

52. S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013). [CrossRef]  

53. L.A. Sordillo, Y. Pu, S. Pratavieira, Y. Budansky, and R.R. Alfano, “Deep optical imaging of tissue using the second and third near-infrared spectral windows,” J. Biomed. Opt. 19(5), 056004 (2014). [CrossRef]  

54. M. V. Padalkar, R. G. Spencer, and N. Pleshko, “Near infrared spectroscopic evaluation of water in hyaline cartilage,” Ann. Biomed. Eng. 41(11), 2426–2436 (2013). [CrossRef]  

55. V. V. Tuchin, “Tissue optics and photonics: light-tissue interaction,” J. Biomed. Photonics Eng. 1(2), 98–134 (2015). [CrossRef]  

56. S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010). [CrossRef]  

57. C. T. Vangsness, J. Huang, and C. F. Smith, “A spectrophotometer analysis of light absorption in the human meniscus,” Clin. Orthop. Relat. Res. 310, 27–29 (1995). [CrossRef]  

58. L. Wang and S. L. Jacques. “Monte Carlo modeling of light transport in multi-layered tissues in standard C,” The University of Texas, MD Anderson Cancer Center, Houston 4(11), (1992).

59. S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016). [CrossRef]  

References

  • View by:

  1. J.A. Buckwalter and H.J. Mankin, “Articular cartilage: Part I,” J. Bone Jt. Surg. 79(4), 600 (1997).
    [Crossref]
  2. A. J. Sophia Fox, A. Bedi, and S.A. Rodeo, “The basic science of articular cartilage: structure, composition, and function,” Sports Health 1(6), 461–468 (2009).
    [Crossref]
  3. E. Jung, H. Choi, M. Lim, H. Kang, H. Park, H. Han, B. Min, S. Kim, I. Park, and H. Lim, “Quantitative analysis of water distribution in human articular cartilage using terahertz time-domain spectroscopy,” Biomed. Opt. Express 3(5), 1110–1115 (2012).
    [Crossref]
  4. M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010).
    [Crossref]
  5. H. Muir, “The chondrocyte, architect of cartilage. Biomechanics, structure, function and molecular biology of cartilage matrix macromolecules,” BioEssays 17(12), 1039–1048 (1995).
    [Crossref]
  6. T. J. Klein, J. Malda, R. L. Sah, and D. W. Hutmacher, “Tissue engineering of articular cartilage with biomimetic zones,” Tissue Eng., Part B 15(2), 143–157 (2009).
    [Crossref]
  7. I. Kafian-Attari, E. Nippolainen, D. Semenov, M. Hauta-Kasari, J. Töyräs, and I. O. Afara, “Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity,” Biomed. Opt. Express 11(11), 6480–6494 (2020).
    [Crossref]
  8. I.O. Afara, I. Prasadam, Z. Arabshahi, Y. Xiao, and A. Oloyede, “Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy,” Sci. Rep. 7(1), 11463–9 (2017).
    [Crossref]
  9. WHO, Chronic Rheumatic Conditions. (2014).
  10. J. C. Buckland-Wright, “Quantitative radiography of osteoarthritis,” Ann. Rheum. Dis. 53(4), 268–275 (1994).
    [Crossref]
  11. L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
    [Crossref]
  12. G. Spahn, H.M. Klinger, and G.O. Hofmann, “How valid is the arthroscopic diagnosis of cartilage lesions? Results of an opinion survey among highly experienced arthroscopic surgeons,” Arch Orthop Trauma Surg 129(8), 1117–1121 (2009).
    [Crossref]
  13. N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
    [Crossref]
  14. F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
    [Crossref]
  15. I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models,” Bone 53(2), 350–357 (2013).
    [Crossref]
  16. J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017).
    [Crossref]
  17. P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
    [Crossref]
  18. G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007).
    [Crossref]
  19. I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
    [Crossref]
  20. U. P Palukuru, C. M. McGoverin, and N. Pleshko, “Assessment of hyaline cartilage matrix composition using near infrared spectroscopy,” Matrix Biol. 38, 3–11 (2014).
    [Crossref]
  21. I. O. Afara, S. Singh, and A. Oloyede, “Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage,” Medical Engineering Physics 35(1), 88–95 (2013).
    [Crossref]
  22. M. Maldonado and J. Nam, “The role of changes in extracellular matrix of cartilage in the presence of inflammation on the pathology of osteoarthritis,” Biomed Research International, 2013.
  23. I. O. Afara, M. Hauta-Kasari, J. S. Jurvelin, A. Oloyede, and J. Töyräs, “Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition,” Physiol. Meas. 36(9), 1913–1928 (2015).
    [Crossref]
  24. G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
    [Crossref]
  25. I. O. Afara, S. Singh, and A. Oloyede, “Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra,” Journal of the mechanical behaviour of biomedical material 20, 249–258 (2013).
    [Crossref]
  26. I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score,” Osteoarthritis and cartilage 20(11), 1367–1373 (2012).
    [Crossref]
  27. L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995).
    [Crossref]
  28. T. Lister, P. Wright, and P. Chappell, “Spectrophotometers for the clinical assessment of port-wine stain skin lesions: a review,” Lasers Med. Sci. 25(3), 449–457 (2010).
    [Crossref]
  29. S. H. Tseng, P. Bargo, A. Durkin, and N. Kollias, “Chromophore concentrations, absorption and scattering properties of human skin in-vivo,” Opt. Express 17(17), 14599–14617 (2009).
    [Crossref]
  30. J. Huang, S. Zhang, S. Gnyawali, C. K. Sen, and R. X. Xu, “Second derivative multispectral algorithm for quantitative assessment of cutaneous tissue oxygenation,” J. Biomed. Opt. 20(3), 036001 (2015).
    [Crossref]
  31. S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
    [Crossref]
  32. X. Chen, W. Lin, C. Wang, S. Chen, J. Sheng, B. Zeng, and M. Xu, “In vivo real-time imaging of cutaneous hemoglobin concentration, oxygen saturation, scattering properties, melanin content, and epidermal thickness with visible spatially modulated light,” Biomed. Opt. Express 8(12), 5468–5482 (2017).
    [Crossref]
  33. E.V. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11(6), 064026 (2006).
    [Crossref]
  34. J. S. Jurvelin, T. Räsänen, P. Kolmonens, and T. Lyyra, “Comparison of optical, needle probe and ultrasonic techniques for the measurement of articular cartilage thickness,” Journal of biomechanics 28(2), 231–235 (1995).
    [Crossref]
  35. X. Zhou, M. J. Ju, L. Huang, and S. Tang, “Slope-based segmentation of articular cartilage using polarization-sensitive optical coherence tomography phase retardation image,” J. Biomed. Opt. 24(03), 1 (2019).
    [Crossref]
  36. M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
    [Crossref]
  37. S. Z. Wang, Y. P. Huang, Q. Wang, Y. P. Zheng, and Y. H. He, “Assessment of depth and degeneration dependences of articular cartilage refractive index using optical coherence tomography in vitro,” Connect. Tissue Res. 51(1), 36–47 (2010).
    [Crossref]
  38. H. C. Van de Hulst, Multiple Light Scattering: Tables, Formulas, and Applications (Elsevier, 2012).
  39. S. Chandrasekhar, Radiative Transfer (Dover Publications, 2013).
  40. S. A. Prahl, M. J. van Gemert, and A. J. Welch, “Determining the optical properties of turbid media by using the adding–doubling method,” Appl. Opt. 32(4), 559–568 (1993).
    [Crossref]
  41. W. Saeys, M. A. Velazco-Roa, S. N. Thennadil, H. Ramon, and B. M. Nicolaï, “Optical properties of apple skin and flesh in the wavelength range from 350 to 2200 nm,” Appl. Opt. 47(7), 908–919 (2008).
    [Crossref]
  42. I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
    [Crossref]
  43. A. F. Mak, W. M. Lai, and V. C. Mow, “Biphasic indentation of articular cartilage—I. Theoretical analysis,” Journal of biomechanics 20(7), 703–714 (1987).
    [Crossref]
  44. A. Thambyah, A. Nather, and J. Goh, “Mechanical properties of articular cartilage covered by the meniscus,” Osteoarthritis and Cartilage 14(6), 580–588 (2006).
    [Crossref]
  45. W. C. Hayes, L. M. Keer, G. Herrmann, and L. F. Mockros, “A mathematical analysis for indentation tests of articular cartilage,” Journal of biomechanics 5(5), 541–551 (1972).
    [Crossref]
  46. J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
    [Crossref]
  47. M. Prakash, J. K. Sarin, L. Rieppo, I. O. Afara, and J. Töyräs, “Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage,” Appl. Spectrosc. 71(10), 2253–2262 (2017).
    [Crossref]
  48. H. J. Mankin, M. E. Johnson, and L. Lippiello, “Biochemical and metabolic abnormalities in articular cartilage from osteoarthritic human hips. III. Distribution and metabolism of amino sugar-containing macromolecules,” JBJS 63(1), 131–139 (1981).
    [Crossref]
  49. I. Kiviranta, J. Jurvelin, A. M. Säämänen, and H. J. Helminen, “Microspectrophotometric quantitation of glycosaminoglycans in articular cartilage sections stained with Safranin O,” Histochemistry 82(3), 249–255 (1985).
    [Crossref]
  50. R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018).
    [Crossref]
  51. R. U. Kleemann, D. Krocker, A. Cedraro, J. Tuischer, and G. N. Duda, “Altered cartilage mechanics and histology in knee osteoarthritis: relation to clinical assessment (ICRS Grade),” Osteoarthritis and cartilage 13(11), 958–963 (2005).
    [Crossref]
  52. S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013).
    [Crossref]
  53. L.A. Sordillo, Y. Pu, S. Pratavieira, Y. Budansky, and R.R. Alfano, “Deep optical imaging of tissue using the second and third near-infrared spectral windows,” J. Biomed. Opt. 19(5), 056004 (2014).
    [Crossref]
  54. M. V. Padalkar, R. G. Spencer, and N. Pleshko, “Near infrared spectroscopic evaluation of water in hyaline cartilage,” Ann. Biomed. Eng. 41(11), 2426–2436 (2013).
    [Crossref]
  55. V. V. Tuchin, “Tissue optics and photonics: light-tissue interaction,” J. Biomed. Photonics Eng. 1(2), 98–134 (2015).
    [Crossref]
  56. S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010).
    [Crossref]
  57. C. T. Vangsness, J. Huang, and C. F. Smith, “A spectrophotometer analysis of light absorption in the human meniscus,” Clin. Orthop. Relat. Res. 310, 27–29 (1995).
    [Crossref]
  58. L. Wang and S. L. Jacques. “Monte Carlo modeling of light transport in multi-layered tissues in standard C,” The University of Texas, MD Anderson Cancer Center, Houston 4(11), (1992).
  59. S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
    [Crossref]

2021 (1)

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

2020 (2)

I. Kafian-Attari, E. Nippolainen, D. Semenov, M. Hauta-Kasari, J. Töyräs, and I. O. Afara, “Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity,” Biomed. Opt. Express 11(11), 6480–6494 (2020).
[Crossref]

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

2019 (2)

X. Zhou, M. J. Ju, L. Huang, and S. Tang, “Slope-based segmentation of articular cartilage using polarization-sensitive optical coherence tomography phase retardation image,” J. Biomed. Opt. 24(03), 1 (2019).
[Crossref]

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

2018 (1)

R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018).
[Crossref]

2017 (5)

M. Prakash, J. K. Sarin, L. Rieppo, I. O. Afara, and J. Töyräs, “Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage,” Appl. Spectrosc. 71(10), 2253–2262 (2017).
[Crossref]

S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
[Crossref]

X. Chen, W. Lin, C. Wang, S. Chen, J. Sheng, B. Zeng, and M. Xu, “In vivo real-time imaging of cutaneous hemoglobin concentration, oxygen saturation, scattering properties, melanin content, and epidermal thickness with visible spatially modulated light,” Biomed. Opt. Express 8(12), 5468–5482 (2017).
[Crossref]

I.O. Afara, I. Prasadam, Z. Arabshahi, Y. Xiao, and A. Oloyede, “Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy,” Sci. Rep. 7(1), 11463–9 (2017).
[Crossref]

J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017).
[Crossref]

2016 (1)

S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
[Crossref]

2015 (4)

V. V. Tuchin, “Tissue optics and photonics: light-tissue interaction,” J. Biomed. Photonics Eng. 1(2), 98–134 (2015).
[Crossref]

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

I. O. Afara, M. Hauta-Kasari, J. S. Jurvelin, A. Oloyede, and J. Töyräs, “Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition,” Physiol. Meas. 36(9), 1913–1928 (2015).
[Crossref]

J. Huang, S. Zhang, S. Gnyawali, C. K. Sen, and R. X. Xu, “Second derivative multispectral algorithm for quantitative assessment of cutaneous tissue oxygenation,” J. Biomed. Opt. 20(3), 036001 (2015).
[Crossref]

2014 (3)

U. P Palukuru, C. M. McGoverin, and N. Pleshko, “Assessment of hyaline cartilage matrix composition using near infrared spectroscopy,” Matrix Biol. 38, 3–11 (2014).
[Crossref]

L.A. Sordillo, Y. Pu, S. Pratavieira, Y. Budansky, and R.R. Alfano, “Deep optical imaging of tissue using the second and third near-infrared spectral windows,” J. Biomed. Opt. 19(5), 056004 (2014).
[Crossref]

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

2013 (6)

M. V. Padalkar, R. G. Spencer, and N. Pleshko, “Near infrared spectroscopic evaluation of water in hyaline cartilage,” Ann. Biomed. Eng. 41(11), 2426–2436 (2013).
[Crossref]

S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013).
[Crossref]

I. O. Afara, S. Singh, and A. Oloyede, “Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage,” Medical Engineering Physics 35(1), 88–95 (2013).
[Crossref]

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models,” Bone 53(2), 350–357 (2013).
[Crossref]

I. O. Afara, S. Singh, and A. Oloyede, “Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra,” Journal of the mechanical behaviour of biomedical material 20, 249–258 (2013).
[Crossref]

2012 (2)

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score,” Osteoarthritis and cartilage 20(11), 1367–1373 (2012).
[Crossref]

E. Jung, H. Choi, M. Lim, H. Kang, H. Park, H. Han, B. Min, S. Kim, I. Park, and H. Lim, “Quantitative analysis of water distribution in human articular cartilage using terahertz time-domain spectroscopy,” Biomed. Opt. Express 3(5), 1110–1115 (2012).
[Crossref]

2010 (5)

M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010).
[Crossref]

L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
[Crossref]

T. Lister, P. Wright, and P. Chappell, “Spectrophotometers for the clinical assessment of port-wine stain skin lesions: a review,” Lasers Med. Sci. 25(3), 449–457 (2010).
[Crossref]

S. Z. Wang, Y. P. Huang, Q. Wang, Y. P. Zheng, and Y. H. He, “Assessment of depth and degeneration dependences of articular cartilage refractive index using optical coherence tomography in vitro,” Connect. Tissue Res. 51(1), 36–47 (2010).
[Crossref]

S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010).
[Crossref]

2009 (4)

S. H. Tseng, P. Bargo, A. Durkin, and N. Kollias, “Chromophore concentrations, absorption and scattering properties of human skin in-vivo,” Opt. Express 17(17), 14599–14617 (2009).
[Crossref]

G. Spahn, H.M. Klinger, and G.O. Hofmann, “How valid is the arthroscopic diagnosis of cartilage lesions? Results of an opinion survey among highly experienced arthroscopic surgeons,” Arch Orthop Trauma Surg 129(8), 1117–1121 (2009).
[Crossref]

A. J. Sophia Fox, A. Bedi, and S.A. Rodeo, “The basic science of articular cartilage: structure, composition, and function,” Sports Health 1(6), 461–468 (2009).
[Crossref]

T. J. Klein, J. Malda, R. L. Sah, and D. W. Hutmacher, “Tissue engineering of articular cartilage with biomimetic zones,” Tissue Eng., Part B 15(2), 143–157 (2009).
[Crossref]

2008 (2)

W. Saeys, M. A. Velazco-Roa, S. N. Thennadil, H. Ramon, and B. M. Nicolaï, “Optical properties of apple skin and flesh in the wavelength range from 350 to 2200 nm,” Appl. Opt. 47(7), 908–919 (2008).
[Crossref]

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

2007 (1)

G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007).
[Crossref]

2006 (2)

E.V. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11(6), 064026 (2006).
[Crossref]

A. Thambyah, A. Nather, and J. Goh, “Mechanical properties of articular cartilage covered by the meniscus,” Osteoarthritis and Cartilage 14(6), 580–588 (2006).
[Crossref]

2005 (1)

R. U. Kleemann, D. Krocker, A. Cedraro, J. Tuischer, and G. N. Duda, “Altered cartilage mechanics and histology in knee osteoarthritis: relation to clinical assessment (ICRS Grade),” Osteoarthritis and cartilage 13(11), 958–963 (2005).
[Crossref]

1997 (1)

J.A. Buckwalter and H.J. Mankin, “Articular cartilage: Part I,” J. Bone Jt. Surg. 79(4), 600 (1997).
[Crossref]

1995 (4)

H. Muir, “The chondrocyte, architect of cartilage. Biomechanics, structure, function and molecular biology of cartilage matrix macromolecules,” BioEssays 17(12), 1039–1048 (1995).
[Crossref]

J. S. Jurvelin, T. Räsänen, P. Kolmonens, and T. Lyyra, “Comparison of optical, needle probe and ultrasonic techniques for the measurement of articular cartilage thickness,” Journal of biomechanics 28(2), 231–235 (1995).
[Crossref]

L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995).
[Crossref]

C. T. Vangsness, J. Huang, and C. F. Smith, “A spectrophotometer analysis of light absorption in the human meniscus,” Clin. Orthop. Relat. Res. 310, 27–29 (1995).
[Crossref]

1994 (1)

J. C. Buckland-Wright, “Quantitative radiography of osteoarthritis,” Ann. Rheum. Dis. 53(4), 268–275 (1994).
[Crossref]

1993 (1)

1991 (1)

F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
[Crossref]

1987 (1)

A. F. Mak, W. M. Lai, and V. C. Mow, “Biphasic indentation of articular cartilage—I. Theoretical analysis,” Journal of biomechanics 20(7), 703–714 (1987).
[Crossref]

1985 (1)

I. Kiviranta, J. Jurvelin, A. M. Säämänen, and H. J. Helminen, “Microspectrophotometric quantitation of glycosaminoglycans in articular cartilage sections stained with Safranin O,” Histochemistry 82(3), 249–255 (1985).
[Crossref]

1981 (1)

H. J. Mankin, M. E. Johnson, and L. Lippiello, “Biochemical and metabolic abnormalities in articular cartilage from osteoarthritic human hips. III. Distribution and metabolism of amino sugar-containing macromolecules,” JBJS 63(1), 131–139 (1981).
[Crossref]

1972 (1)

W. C. Hayes, L. M. Keer, G. Herrmann, and L. F. Mockros, “A mathematical analysis for indentation tests of articular cartilage,” Journal of biomechanics 5(5), 541–551 (1972).
[Crossref]

Afara, I. O.

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

I. Kafian-Attari, E. Nippolainen, D. Semenov, M. Hauta-Kasari, J. Töyräs, and I. O. Afara, “Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity,” Biomed. Opt. Express 11(11), 6480–6494 (2020).
[Crossref]

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

M. Prakash, J. K. Sarin, L. Rieppo, I. O. Afara, and J. Töyräs, “Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage,” Appl. Spectrosc. 71(10), 2253–2262 (2017).
[Crossref]

J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017).
[Crossref]

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

I. O. Afara, M. Hauta-Kasari, J. S. Jurvelin, A. Oloyede, and J. Töyräs, “Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition,” Physiol. Meas. 36(9), 1913–1928 (2015).
[Crossref]

I. O. Afara, S. Singh, and A. Oloyede, “Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage,” Medical Engineering Physics 35(1), 88–95 (2013).
[Crossref]

I. O. Afara, S. Singh, and A. Oloyede, “Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra,” Journal of the mechanical behaviour of biomedical material 20, 249–258 (2013).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models,” Bone 53(2), 350–357 (2013).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score,” Osteoarthritis and cartilage 20(11), 1367–1373 (2012).
[Crossref]

Afara, I.O.

I.O. Afara, I. Prasadam, Z. Arabshahi, Y. Xiao, and A. Oloyede, “Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy,” Sci. Rep. 7(1), 11463–9 (2017).
[Crossref]

Alfano, R.R.

L.A. Sordillo, Y. Pu, S. Pratavieira, Y. Budansky, and R.R. Alfano, “Deep optical imaging of tissue using the second and third near-infrared spectral windows,” J. Biomed. Opt. 19(5), 056004 (2014).
[Crossref]

Arabshahi, Z.

I.O. Afara, I. Prasadam, Z. Arabshahi, Y. Xiao, and A. Oloyede, “Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy,” Sci. Rep. 7(1), 11463–9 (2017).
[Crossref]

Bargo, P.

Bedi, A.

A. J. Sophia Fox, A. Bedi, and S.A. Rodeo, “The basic science of articular cartilage: structure, composition, and function,” Sports Health 1(6), 461–468 (2009).
[Crossref]

Berns, M. W.

L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995).
[Crossref]

Boppart, S. A.

S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
[Crossref]

Bouillon, B.

L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
[Crossref]

Brill, N.

S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
[Crossref]

Brommer, H.

J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017).
[Crossref]

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

Brown, J. Q.

S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
[Crossref]

Buckland-Wright, J. C.

J. C. Buckland-Wright, “Quantitative radiography of osteoarthritis,” Ann. Rheum. Dis. 53(4), 268–275 (1994).
[Crossref]

Buckwalter, J.A.

J.A. Buckwalter and H.J. Mankin, “Articular cartilage: Part I,” J. Bone Jt. Surg. 79(4), 600 (1997).
[Crossref]

Budansky, Y.

L.A. Sordillo, Y. Pu, S. Pratavieira, Y. Budansky, and R.R. Alfano, “Deep optical imaging of tissue using the second and third near-infrared spectral windows,” J. Biomed. Opt. 19(5), 056004 (2014).
[Crossref]

Cedraro, A.

R. U. Kleemann, D. Krocker, A. Cedraro, J. Tuischer, and G. N. Duda, “Altered cartilage mechanics and histology in knee osteoarthritis: relation to clinical assessment (ICRS Grade),” Osteoarthritis and cartilage 13(11), 958–963 (2005).
[Crossref]

Chandrasekhar, S.

S. Chandrasekhar, Radiative Transfer (Dover Publications, 2013).

Chappell, P.

T. Lister, P. Wright, and P. Chappell, “Spectrophotometers for the clinical assessment of port-wine stain skin lesions: a review,” Lasers Med. Sci. 25(3), 449–457 (2010).
[Crossref]

Chen, S.

Chen, X.

Choi, H.

Crawford, R.

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models,” Bone 53(2), 350–357 (2013).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score,” Osteoarthritis and cartilage 20(11), 1367–1373 (2012).
[Crossref]

D’Lima, D.

M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010).
[Crossref]

Dàvid, A.

L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
[Crossref]

Davies, C. D. L.

R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018).
[Crossref]

Drogset, J. O.

R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018).
[Crossref]

Duda, G. N.

R. U. Kleemann, D. Krocker, A. Cedraro, J. Tuischer, and G. N. Duda, “Altered cartilage mechanics and histology in knee osteoarthritis: relation to clinical assessment (ICRS Grade),” Osteoarthritis and cartilage 13(11), 958–963 (2005).
[Crossref]

Durkin, A.

Farah, C. S.

S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
[Crossref]

Faris, F.

F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
[Crossref]

Finnilä, M. A.

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

Fiskerstrand, E. J.

L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995).
[Crossref]

Gnyawali, S.

J. Huang, S. Zhang, S. Gnyawali, C. K. Sen, and R. X. Xu, “Second derivative multispectral algorithm for quantitative assessment of cutaneous tissue oxygenation,” J. Biomed. Opt. 20(3), 036001 (2015).
[Crossref]

Goh, J.

A. Thambyah, A. Nather, and J. Goh, “Mechanical properties of articular cartilage covered by the meniscus,” Osteoarthritis and Cartilage 14(6), 580–588 (2006).
[Crossref]

Grogan, S.P.

M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010).
[Crossref]

Günther, M.

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

Haage, P.

L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
[Crossref]

Han, H.

Han, S. K.

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

Hauta-Kasari, M.

I. Kafian-Attari, E. Nippolainen, D. Semenov, M. Hauta-Kasari, J. Töyräs, and I. O. Afara, “Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity,” Biomed. Opt. Express 11(11), 6480–6494 (2020).
[Crossref]

I. O. Afara, M. Hauta-Kasari, J. S. Jurvelin, A. Oloyede, and J. Töyräs, “Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition,” Physiol. Meas. 36(9), 1913–1928 (2015).
[Crossref]

Hayes, W. C.

W. C. Hayes, L. M. Keer, G. Herrmann, and L. F. Mockros, “A mathematical analysis for indentation tests of articular cartilage,” Journal of biomechanics 5(5), 541–551 (1972).
[Crossref]

He, Y. H.

S. Z. Wang, Y. P. Huang, Q. Wang, Y. P. Zheng, and Y. H. He, “Assessment of depth and degeneration dependences of articular cartilage refractive index using optical coherence tomography in vitro,” Connect. Tissue Res. 51(1), 36–47 (2010).
[Crossref]

Helminen, H. J.

I. Kiviranta, J. Jurvelin, A. M. Säämänen, and H. J. Helminen, “Microspectrophotometric quantitation of glycosaminoglycans in articular cartilage sections stained with Safranin O,” Histochemistry 82(3), 249–255 (1985).
[Crossref]

Herrmann, G.

W. C. Hayes, L. M. Keer, G. Herrmann, and L. F. Mockros, “A mathematical analysis for indentation tests of articular cartilage,” Journal of biomechanics 5(5), 541–551 (1972).
[Crossref]

Herzog, W.

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

Hofmann, G. O.

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007).
[Crossref]

Hofmann, G.O.

G. Spahn, H.M. Klinger, and G.O. Hofmann, “How valid is the arthroscopic diagnosis of cartilage lesions? Results of an opinion survey among highly experienced arthroscopic surgeons,” Arch Orthop Trauma Surg 129(8), 1117–1121 (2009).
[Crossref]

Honkanen, M. K. M.

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

Houston, R.

F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
[Crossref]

Huang, J.

J. Huang, S. Zhang, S. Gnyawali, C. K. Sen, and R. X. Xu, “Second derivative multispectral algorithm for quantitative assessment of cutaneous tissue oxygenation,” J. Biomed. Opt. 20(3), 036001 (2015).
[Crossref]

C. T. Vangsness, J. Huang, and C. F. Smith, “A spectrophotometer analysis of light absorption in the human meniscus,” Clin. Orthop. Relat. Res. 310, 27–29 (1995).
[Crossref]

Huang, L.

X. Zhou, M. J. Ju, L. Huang, and S. Tang, “Slope-based segmentation of articular cartilage using polarization-sensitive optical coherence tomography phase retardation image,” J. Biomed. Opt. 24(03), 1 (2019).
[Crossref]

Huang, Y. P.

S. Z. Wang, Y. P. Huang, Q. Wang, Y. P. Zheng, and Y. H. He, “Assessment of depth and degeneration dependences of articular cartilage refractive index using optical coherence tomography in vitro,” Connect. Tissue Res. 51(1), 36–47 (2010).
[Crossref]

Hutmacher, D. W.

T. J. Klein, J. Malda, R. L. Sah, and D. W. Hutmacher, “Tissue engineering of articular cartilage with biomimetic zones,” Tissue Eng., Part B 15(2), 143–157 (2009).
[Crossref]

Isaksen, V.

R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018).
[Crossref]

Jacques, S. L.

S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013).
[Crossref]

L. Wang and S. L. Jacques. “Monte Carlo modeling of light transport in multi-layered tissues in standard C,” The University of Texas, MD Anderson Cancer Center, Houston 4(11), (1992).

Jahr, H.

S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
[Crossref]

Jiang, B.

E.V. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11(6), 064026 (2006).
[Crossref]

Johnson, M. E.

H. J. Mankin, M. E. Johnson, and L. Lippiello, “Biochemical and metabolic abnormalities in articular cartilage from osteoarthritic human hips. III. Distribution and metabolism of amino sugar-containing macromolecules,” JBJS 63(1), 131–139 (1981).
[Crossref]

Joukainen, A.

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

Ju, M. J.

X. Zhou, M. J. Ju, L. Huang, and S. Tang, “Slope-based segmentation of articular cartilage using polarization-sensitive optical coherence tomography phase retardation image,” J. Biomed. Opt. 24(03), 1 (2019).
[Crossref]

Julkunen, P.

S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010).
[Crossref]

Jung, E.

Jurvelin, J.

I. Kiviranta, J. Jurvelin, A. M. Säämänen, and H. J. Helminen, “Microspectrophotometric quantitation of glycosaminoglycans in articular cartilage sections stained with Safranin O,” Histochemistry 82(3), 249–255 (1985).
[Crossref]

Jurvelin, J. S.

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

I. O. Afara, M. Hauta-Kasari, J. S. Jurvelin, A. Oloyede, and J. Töyräs, “Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition,” Physiol. Meas. 36(9), 1913–1928 (2015).
[Crossref]

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010).
[Crossref]

J. S. Jurvelin, T. Räsänen, P. Kolmonens, and T. Lyyra, “Comparison of optical, needle probe and ultrasonic techniques for the measurement of articular cartilage thickness,” Journal of biomechanics 28(2), 231–235 (1995).
[Crossref]

Kafian-Attari, I.

Kahl, E.

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007).
[Crossref]

Kandel, S.

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

Kang, H.

Keer, L. M.

W. C. Hayes, L. M. Keer, G. Herrmann, and L. F. Mockros, “A mathematical analysis for indentation tests of articular cartilage,” Journal of biomechanics 5(5), 541–551 (1972).
[Crossref]

Kho, E.

S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
[Crossref]

Kim, S.

Kiviranta, I.

I. Kiviranta, J. Jurvelin, A. M. Säämänen, and H. J. Helminen, “Microspectrophotometric quantitation of glycosaminoglycans in articular cartilage sections stained with Safranin O,” Histochemistry 82(3), 249–255 (1985).
[Crossref]

Kiviranta, P.

S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010).
[Crossref]

Kleemann, R. U.

R. U. Kleemann, D. Krocker, A. Cedraro, J. Tuischer, and G. N. Duda, “Altered cartilage mechanics and histology in knee osteoarthritis: relation to clinical assessment (ICRS Grade),” Osteoarthritis and cartilage 13(11), 958–963 (2005).
[Crossref]

Klein, T. J.

T. J. Klein, J. Malda, R. L. Sah, and D. W. Hutmacher, “Tissue engineering of articular cartilage with biomimetic zones,” Tissue Eng., Part B 15(2), 143–157 (2009).
[Crossref]

Klinger, H. M.

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

Klinger, H.M.

G. Spahn, H.M. Klinger, and G.O. Hofmann, “How valid is the arthroscopic diagnosis of cartilage lesions? Results of an opinion survey among highly experienced arthroscopic surgeons,” Arch Orthop Trauma Surg 129(8), 1117–1121 (2009).
[Crossref]

G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007).
[Crossref]

Klussmann, A.

L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
[Crossref]

Kollias, N.

Kolmonens, P.

J. S. Jurvelin, T. Räsänen, P. Kolmonens, and T. Lyyra, “Comparison of optical, needle probe and ultrasonic techniques for the measurement of articular cartilage thickness,” Journal of biomechanics 28(2), 231–235 (1995).
[Crossref]

Korhonen, R. K.

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010).
[Crossref]

Krocker, D.

R. U. Kleemann, D. Krocker, A. Cedraro, J. Tuischer, and G. N. Duda, “Altered cartilage mechanics and histology in knee osteoarthritis: relation to clinical assessment (ICRS Grade),” Osteoarthritis and cartilage 13(11), 958–963 (2005).
[Crossref]

Kröger, H.

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

Kuhl, C.

S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
[Crossref]

Kumar, R.

R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018).
[Crossref]

Lahner, M.

L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
[Crossref]

Lai, W. M.

A. F. Mak, W. M. Lai, and V. C. Mow, “Biphasic indentation of articular cartilage—I. Theoretical analysis,” Journal of biomechanics 20(7), 703–714 (1987).
[Crossref]

Lichtinger, T. K.

L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
[Crossref]

Lilledahl, M. B.

R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018).
[Crossref]

Lim, H.

Lim, M.

Lin, W.

Lippiello, L.

H. J. Mankin, M. E. Johnson, and L. Lippiello, “Biochemical and metabolic abnormalities in articular cartilage from osteoarthritic human hips. III. Distribution and metabolism of amino sugar-containing macromolecules,” JBJS 63(1), 131–139 (1981).
[Crossref]

Lister, T.

T. Lister, P. Wright, and P. Chappell, “Spectrophotometers for the clinical assessment of port-wine stain skin lesions: a review,” Lasers Med. Sci. 25(3), 449–457 (2010).
[Crossref]

Liukkonen, J.

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

Livera, N.

F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
[Crossref]

Lotz, M.K.

M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010).
[Crossref]

Lyyra, T.

J. S. Jurvelin, T. Räsänen, P. Kolmonens, and T. Lyyra, “Comparison of optical, needle probe and ultrasonic techniques for the measurement of articular cartilage thickness,” Journal of biomechanics 28(2), 231–235 (1995).
[Crossref]

Madden, R.

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

Mak, A. F.

A. F. Mak, W. M. Lai, and V. C. Mow, “Biphasic indentation of articular cartilage—I. Theoretical analysis,” Journal of biomechanics 20(7), 703–714 (1987).
[Crossref]

Mäkelä, J. T. A.

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

Mäkitalo, J.

S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010).
[Crossref]

Malda, J.

T. J. Klein, J. Malda, R. L. Sah, and D. W. Hutmacher, “Tissue engineering of articular cartilage with biomimetic zones,” Tissue Eng., Part B 15(2), 143–157 (2009).
[Crossref]

Maldonado, M.

M. Maldonado and J. Nam, “The role of changes in extracellular matrix of cartilage in the presence of inflammation on the pathology of osteoarthritis,” Biomed Research International, 2013.

Mankin, H. J.

H. J. Mankin, M. E. Johnson, and L. Lippiello, “Biochemical and metabolic abnormalities in articular cartilage from osteoarthritic human hips. III. Distribution and metabolism of amino sugar-containing macromolecules,” JBJS 63(1), 131–139 (1981).
[Crossref]

Mankin, H.J.

J.A. Buckwalter and H.J. Mankin, “Articular cartilage: Part I,” J. Bone Jt. Surg. 79(4), 600 (1997).
[Crossref]

Marcu, L.

S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
[Crossref]

McGoverin, C. M.

U. P Palukuru, C. M. McGoverin, and N. Pleshko, “Assessment of hyaline cartilage matrix composition using near infrared spectroscopy,” Matrix Biol. 38, 3–11 (2014).
[Crossref]

Mikkonen, S.

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

Min, B.

Mockros, L. F.

W. C. Hayes, L. M. Keer, G. Herrmann, and L. F. Mockros, “A mathematical analysis for indentation tests of articular cartilage,” Journal of biomechanics 5(5), 541–551 (1972).
[Crossref]

Mollenhauer, J. A.

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

Mow, V. C.

A. F. Mak, W. M. Lai, and V. C. Mow, “Biphasic indentation of articular cartilage—I. Theoretical analysis,” Journal of biomechanics 20(7), 703–714 (1987).
[Crossref]

Mückley, T.

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007).
[Crossref]

Muir, H.

H. Muir, “The chondrocyte, architect of cartilage. Biomechanics, structure, function and molecular biology of cartilage matrix macromolecules,” BioEssays 17(12), 1039–1048 (1995).
[Crossref]

Nagel, H.

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

Nam, J.

M. Maldonado and J. Nam, “The role of changes in extracellular matrix of cartilage in the presence of inflammation on the pathology of osteoarthritis,” Biomed Research International, 2013.

Nather, A.

A. Thambyah, A. Nather, and J. Goh, “Mechanical properties of articular cartilage covered by the meniscus,” Osteoarthritis and Cartilage 14(6), 580–588 (2006).
[Crossref]

Nebelung, S.

S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
[Crossref]

Nelson, J. S.

L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995).
[Crossref]

Nicolaï, B. M.

Nippolainen, E.

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

I. Kafian-Attari, E. Nippolainen, D. Semenov, M. Hauta-Kasari, J. Töyräs, and I. O. Afara, “Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity,” Biomed. Opt. Express 11(11), 6480–6494 (2020).
[Crossref]

Norvang, L. T.

L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995).
[Crossref]

Novak, J.

E.V. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11(6), 064026 (2006).
[Crossref]

Ojanen, S.

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

Oloyede, A.

I.O. Afara, I. Prasadam, Z. Arabshahi, Y. Xiao, and A. Oloyede, “Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy,” Sci. Rep. 7(1), 11463–9 (2017).
[Crossref]

I. O. Afara, M. Hauta-Kasari, J. S. Jurvelin, A. Oloyede, and J. Töyräs, “Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition,” Physiol. Meas. 36(9), 1913–1928 (2015).
[Crossref]

I. O. Afara, S. Singh, and A. Oloyede, “Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra,” Journal of the mechanical behaviour of biomedical material 20, 249–258 (2013).
[Crossref]

I. O. Afara, S. Singh, and A. Oloyede, “Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage,” Medical Engineering Physics 35(1), 88–95 (2013).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models,” Bone 53(2), 350–357 (2013).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score,” Osteoarthritis and cartilage 20(11), 1367–1373 (2012).
[Crossref]

Otsuki, S.

M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010).
[Crossref]

Padalkar, M. V.

M. V. Padalkar, R. G. Spencer, and N. Pleshko, “Near infrared spectroscopic evaluation of water in hyaline cartilage,” Ann. Biomed. Eng. 41(11), 2426–2436 (2013).
[Crossref]

Palukuru, U. P

U. P Palukuru, C. M. McGoverin, and N. Pleshko, “Assessment of hyaline cartilage matrix composition using near infrared spectroscopy,” Matrix Biol. 38, 3–11 (2014).
[Crossref]

Park, H.

Park, I.

Pierce, D. M.

R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018).
[Crossref]

Pleshko, N.

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

U. P Palukuru, C. M. McGoverin, and N. Pleshko, “Assessment of hyaline cartilage matrix composition using near infrared spectroscopy,” Matrix Biol. 38, 3–11 (2014).
[Crossref]

M. V. Padalkar, R. G. Spencer, and N. Pleshko, “Near infrared spectroscopic evaluation of water in hyaline cartilage,” Ann. Biomed. Eng. 41(11), 2426–2436 (2013).
[Crossref]

Plettenberg, H.

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007).
[Crossref]

Prahl, S. A.

Prakash, M.

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

M. Prakash, J. K. Sarin, L. Rieppo, I. O. Afara, and J. Töyräs, “Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage,” Appl. Spectrosc. 71(10), 2253–2262 (2017).
[Crossref]

Prasadam, I.

I.O. Afara, I. Prasadam, Z. Arabshahi, Y. Xiao, and A. Oloyede, “Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy,” Sci. Rep. 7(1), 11463–9 (2017).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models,” Bone 53(2), 350–357 (2013).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score,” Osteoarthritis and cartilage 20(11), 1367–1373 (2012).
[Crossref]

Pratavieira, S.

L.A. Sordillo, Y. Pu, S. Pratavieira, Y. Budansky, and R.R. Alfano, “Deep optical imaging of tissue using the second and third near-infrared spectral windows,” J. Biomed. Opt. 19(5), 056004 (2014).
[Crossref]

Pu, Y.

L.A. Sordillo, Y. Pu, S. Pratavieira, Y. Budansky, and R.R. Alfano, “Deep optical imaging of tissue using the second and third near-infrared spectral windows,” J. Biomed. Opt. 19(5), 056004 (2014).
[Crossref]

Pufe, T.

S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
[Crossref]

Puhakka, P. H.

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

Querido, W.

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

Ramon, H.

Räsänen, T.

J. S. Jurvelin, T. Räsänen, P. Kolmonens, and T. Lyyra, “Comparison of optical, needle probe and ultrasonic techniques for the measurement of articular cartilage thickness,” Journal of biomechanics 28(2), 231–235 (1995).
[Crossref]

Rezaeian, Z. S.

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

Rieppo, L.

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

M. Prakash, J. K. Sarin, L. Rieppo, I. O. Afara, and J. Töyräs, “Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage,” Appl. Spectrosc. 71(10), 2253–2262 (2017).
[Crossref]

J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017).
[Crossref]

Rodeo, S.A.

A. J. Sophia Fox, A. Bedi, and S.A. Rodeo, “The basic science of articular cartilage: structure, composition, and function,” Sports Health 1(6), 461–468 (2009).
[Crossref]

Rolfe, P.

F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
[Crossref]

Säämänen, A. M.

I. Kiviranta, J. Jurvelin, A. M. Säämänen, and H. J. Helminen, “Microspectrophotometric quantitation of glycosaminoglycans in articular cartilage sections stained with Safranin O,” Histochemistry 82(3), 249–255 (1985).
[Crossref]

Saarakkala, S.

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017).
[Crossref]

S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010).
[Crossref]

Saeys, W.

Sah, R.

M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010).
[Crossref]

Sah, R. L.

T. J. Klein, J. Malda, R. L. Sah, and D. W. Hutmacher, “Tissue engineering of articular cartilage with biomimetic zones,” Tissue Eng., Part B 15(2), 143–157 (2009).
[Crossref]

Salomatina, E.V.

E.V. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11(6), 064026 (2006).
[Crossref]

Sarin, J. K.

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

M. Prakash, J. K. Sarin, L. Rieppo, I. O. Afara, and J. Töyräs, “Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage,” Appl. Spectrosc. 71(10), 2253–2262 (2017).
[Crossref]

J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017).
[Crossref]

Saunders, C. M.

S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
[Crossref]

Semenov, D.

Sen, C. K.

J. Huang, S. Zhang, S. Gnyawali, C. K. Sen, and R. X. Xu, “Second derivative multispectral algorithm for quantitative assessment of cutaneous tissue oxygenation,” J. Biomed. Opt. 20(3), 036001 (2015).
[Crossref]

Shaikh, R.

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

Sheng, J.

Singh, S.

I. O. Afara, S. Singh, and A. Oloyede, “Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage,” Medical Engineering Physics 35(1), 88–95 (2013).
[Crossref]

I. O. Afara, S. Singh, and A. Oloyede, “Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra,” Journal of the mechanical behaviour of biomedical material 20, 249–258 (2013).
[Crossref]

Smith, C. F.

C. T. Vangsness, J. Huang, and C. F. Smith, “A spectrophotometer analysis of light absorption in the human meniscus,” Clin. Orthop. Relat. Res. 310, 27–29 (1995).
[Crossref]

Sophia Fox, A. J.

A. J. Sophia Fox, A. Bedi, and S.A. Rodeo, “The basic science of articular cartilage: structure, composition, and function,” Sports Health 1(6), 461–468 (2009).
[Crossref]

Sordillo, L.A.

L.A. Sordillo, Y. Pu, S. Pratavieira, Y. Budansky, and R.R. Alfano, “Deep optical imaging of tissue using the second and third near-infrared spectral windows,” J. Biomed. Opt. 19(5), 056004 (2014).
[Crossref]

Spahn, G.

G. Spahn, H.M. Klinger, and G.O. Hofmann, “How valid is the arthroscopic diagnosis of cartilage lesions? Results of an opinion survey among highly experienced arthroscopic surgeons,” Arch Orthop Trauma Surg 129(8), 1117–1121 (2009).
[Crossref]

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007).
[Crossref]

Spencer, A.

F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
[Crossref]

Spencer, R. G.

M. V. Padalkar, R. G. Spencer, and N. Pleshko, “Near infrared spectroscopic evaluation of water in hyaline cartilage,” Ann. Biomed. Eng. 41(11), 2426–2436 (2013).
[Crossref]

Sterenborg, H. J.

S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
[Crossref]

Stopps, E. K. S.

L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995).
[Crossref]

Svaasand, L. O.

L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995).
[Crossref]

Tang, S.

X. Zhou, M. J. Ju, L. Huang, and S. Tang, “Slope-based segmentation of articular cartilage using polarization-sensitive optical coherence tomography phase retardation image,” J. Biomed. Opt. 24(03), 1 (2019).
[Crossref]

Te Moller, N. C. R.

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

Terkeltaub, R.

M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010).
[Crossref]

Thambyah, A.

A. Thambyah, A. Nather, and J. Goh, “Mechanical properties of articular cartilage covered by the meniscus,” Osteoarthritis and Cartilage 14(6), 580–588 (2006).
[Crossref]

Thennadil, S. N.

Thorniley, M.

F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
[Crossref]

Tiitu, V.

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

Timonen, M.

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

Tingart, M.

S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
[Crossref]

Torniainen, J.

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

Töyräs, J.

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

I. Kafian-Attari, E. Nippolainen, D. Semenov, M. Hauta-Kasari, J. Töyräs, and I. O. Afara, “Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity,” Biomed. Opt. Express 11(11), 6480–6494 (2020).
[Crossref]

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

M. Prakash, J. K. Sarin, L. Rieppo, I. O. Afara, and J. Töyräs, “Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage,” Appl. Spectrosc. 71(10), 2253–2262 (2017).
[Crossref]

J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017).
[Crossref]

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

I. O. Afara, M. Hauta-Kasari, J. S. Jurvelin, A. Oloyede, and J. Töyräs, “Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition,” Physiol. Meas. 36(9), 1913–1928 (2015).
[Crossref]

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

Truhn, D.

S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
[Crossref]

Tseng, S. H.

Tuchin, V. V.

V. V. Tuchin, “Tissue optics and photonics: light-tissue interaction,” J. Biomed. Photonics Eng. 1(2), 98–134 (2015).
[Crossref]

Tuischer, J.

R. U. Kleemann, D. Krocker, A. Cedraro, J. Tuischer, and G. N. Duda, “Altered cartilage mechanics and histology in knee osteoarthritis: relation to clinical assessment (ICRS Grade),” Osteoarthritis and cartilage 13(11), 958–963 (2005).
[Crossref]

Van de Hulst, H. C.

H. C. Van de Hulst, Multiple Light Scattering: Tables, Formulas, and Applications (Elsevier, 2012).

van Gemert, M. J.

Van Weeren, P. R.

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

Vangsness, C. T.

C. T. Vangsness, J. Huang, and C. F. Smith, “A spectrophotometer analysis of light absorption in the human meniscus,” Clin. Orthop. Relat. Res. 310, 27–29 (1995).
[Crossref]

Velazco-Roa, M. A.

Virén, T.

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

von Engelhardt, L. V.

L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
[Crossref]

Wang, C.

Wang, L.

L. Wang and S. L. Jacques. “Monte Carlo modeling of light transport in multi-layered tissues in standard C,” The University of Texas, MD Anderson Cancer Center, Houston 4(11), (1992).

Wang, Q.

S. Z. Wang, Y. P. Huang, Q. Wang, Y. P. Zheng, and Y. H. He, “Assessment of depth and degeneration dependences of articular cartilage refractive index using optical coherence tomography in vitro,” Connect. Tissue Res. 51(1), 36–47 (2010).
[Crossref]

Wang, S. Z.

S. Z. Wang, Y. P. Huang, Q. Wang, Y. P. Zheng, and Y. H. He, “Assessment of depth and degeneration dependences of articular cartilage refractive index using optical coherence tomography in vitro,” Connect. Tissue Res. 51(1), 36–47 (2010).
[Crossref]

Welch, A. J.

Wickramasinghe, Y. A. B.D.

F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
[Crossref]

Wright, P.

T. Lister, P. Wright, and P. Chappell, “Spectrophotometers for the clinical assessment of port-wine stain skin lesions: a review,” Lasers Med. Sci. 25(3), 449–457 (2010).
[Crossref]

Xiao, Y.

I.O. Afara, I. Prasadam, Z. Arabshahi, Y. Xiao, and A. Oloyede, “Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy,” Sci. Rep. 7(1), 11463–9 (2017).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models,” Bone 53(2), 350–357 (2013).
[Crossref]

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score,” Osteoarthritis and cartilage 20(11), 1367–1373 (2012).
[Crossref]

Xu, M.

Xu, R. X.

J. Huang, S. Zhang, S. Gnyawali, C. K. Sen, and R. X. Xu, “Second derivative multispectral algorithm for quantitative assessment of cutaneous tissue oxygenation,” J. Biomed. Opt. 20(3), 036001 (2015).
[Crossref]

Yaroslavsky, A. N.

E.V. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11(6), 064026 (2006).
[Crossref]

Zeng, B.

Zhang, S.

J. Huang, S. Zhang, S. Gnyawali, C. K. Sen, and R. X. Xu, “Second derivative multispectral algorithm for quantitative assessment of cutaneous tissue oxygenation,” J. Biomed. Opt. 20(3), 036001 (2015).
[Crossref]

Zheng, Y. P.

S. Z. Wang, Y. P. Huang, Q. Wang, Y. P. Zheng, and Y. H. He, “Assessment of depth and degeneration dependences of articular cartilage refractive index using optical coherence tomography in vitro,” Connect. Tissue Res. 51(1), 36–47 (2010).
[Crossref]

Zhou, X.

X. Zhou, M. J. Ju, L. Huang, and S. Tang, “Slope-based segmentation of articular cartilage using polarization-sensitive optical coherence tomography phase retardation image,” J. Biomed. Opt. 24(03), 1 (2019).
[Crossref]

Ann. Biomed. Eng. (1)

M. V. Padalkar, R. G. Spencer, and N. Pleshko, “Near infrared spectroscopic evaluation of water in hyaline cartilage,” Ann. Biomed. Eng. 41(11), 2426–2436 (2013).
[Crossref]

Ann. Rheum. Dis. (1)

J. C. Buckland-Wright, “Quantitative radiography of osteoarthritis,” Ann. Rheum. Dis. 53(4), 268–275 (1994).
[Crossref]

Appl. Opt. (2)

Appl. Spectrosc. (1)

Arch Orthop Trauma Surg (1)

G. Spahn, H.M. Klinger, and G.O. Hofmann, “How valid is the arthroscopic diagnosis of cartilage lesions? Results of an opinion survey among highly experienced arthroscopic surgeons,” Arch Orthop Trauma Surg 129(8), 1117–1121 (2009).
[Crossref]

Arthritis Rheum. (1)

M.K. Lotz, S. Otsuki, S.P. Grogan, R. Sah, R. Terkeltaub, and D. D’Lima, “Cartilage cell clusters,” Arthritis Rheum. 62(8), 2206–2218 (2010).
[Crossref]

BioEssays (1)

H. Muir, “The chondrocyte, architect of cartilage. Biomechanics, structure, function and molecular biology of cartilage matrix macromolecules,” BioEssays 17(12), 1039–1048 (1995).
[Crossref]

Biomed. Opt. Express (3)

BMC Musculoskeletal Disord. (2)

G. Spahn, H. Plettenberg, E. Kahl, H.M. Klinger, T. Mückley, and G. O. Hofmann, “Near-infrared (NIR) spectroscopy. A new method for arthroscopic evaluation of low grade degenerated cartilage lesions. Results of a pilot study,” BMC Musculoskeletal Disord. 8(1), 47 (2007).
[Crossref]

L. V. von Engelhardt, M. Lahner, A. Klussmann, B. Bouillon, A. Dàvid, P. Haage, and T. K. Lichtinger, “Arthroscopy vs. MRI for a detailed assessment of cartilage disease in osteoarthritis: diagnostic value of MRI in clinical practice,” BMC Musculoskeletal Disord. 11(1), 1–8 (2010).
[Crossref]

Bone (1)

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models,” Bone 53(2), 350–357 (2013).
[Crossref]

Cell. Mol. Bioeng. (1)

I. O. Afara, J. K. Sarin, S. Ojanen, M. A. Finnilä, W. Herzog, S. Saarakkala, R. K. Korhonen, and J. Töyräs, “Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy,” Cell. Mol. Bioeng. 13(3), 219–228 (2020).
[Crossref]

Clin. Orthop. Relat. Res. (1)

C. T. Vangsness, J. Huang, and C. F. Smith, “A spectrophotometer analysis of light absorption in the human meniscus,” Clin. Orthop. Relat. Res. 310, 27–29 (1995).
[Crossref]

Clin. Phys. Physiol. Meas. (1)

F. Faris, M. Thorniley, Y. A. B.D. Wickramasinghe, R. Houston, P. Rolfe, N. Livera, and A. Spencer, “Non-invasive in vivo near-infrared optical measurement of the penetration depth in the neonatal head,” Clin. Phys. Physiol. Meas. 12(4), 353–358 (1991).
[Crossref]

Connect. Tissue Res. (1)

S. Z. Wang, Y. P. Huang, Q. Wang, Y. P. Zheng, and Y. H. He, “Assessment of depth and degeneration dependences of articular cartilage refractive index using optical coherence tomography in vitro,” Connect. Tissue Res. 51(1), 36–47 (2010).
[Crossref]

Histochemistry (1)

I. Kiviranta, J. Jurvelin, A. M. Säämänen, and H. J. Helminen, “Microspectrophotometric quantitation of glycosaminoglycans in articular cartilage sections stained with Safranin O,” Histochemistry 82(3), 249–255 (1985).
[Crossref]

Int. J. Mol. Sci. (1)

R. Kumar, D. M. Pierce, V. Isaksen, C. D. L. Davies, J. O. Drogset, and M. B. Lilledahl, “Comparison of compressive stress-relaxation behavior in osteoarthritic (ICRS graded) human articular cartilage,” Int. J. Mol. Sci. 19(2), 413 (2018).
[Crossref]

J. Biomed. Opt. (5)

L.A. Sordillo, Y. Pu, S. Pratavieira, Y. Budansky, and R.R. Alfano, “Deep optical imaging of tissue using the second and third near-infrared spectral windows,” J. Biomed. Opt. 19(5), 056004 (2014).
[Crossref]

X. Zhou, M. J. Ju, L. Huang, and S. Tang, “Slope-based segmentation of articular cartilage using polarization-sensitive optical coherence tomography phase retardation image,” J. Biomed. Opt. 24(03), 1 (2019).
[Crossref]

E.V. Salomatina, B. Jiang, J. Novak, and A. N. Yaroslavsky, “Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range,” J. Biomed. Opt. 11(6), 064026 (2006).
[Crossref]

J. Huang, S. Zhang, S. Gnyawali, C. K. Sen, and R. X. Xu, “Second derivative multispectral algorithm for quantitative assessment of cutaneous tissue oxygenation,” J. Biomed. Opt. 20(3), 036001 (2015).
[Crossref]

S. A. Boppart, J. Q. Brown, C. S. Farah, E. Kho, L. Marcu, C. M. Saunders, and H. J. Sterenborg, “Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment,” J. Biomed. Opt. 23(02), 1 (2017).
[Crossref]

J. Biomed. Photonics Eng. (1)

V. V. Tuchin, “Tissue optics and photonics: light-tissue interaction,” J. Biomed. Photonics Eng. 1(2), 98–134 (2015).
[Crossref]

J. Bone Jt. Surg. (1)

J.A. Buckwalter and H.J. Mankin, “Articular cartilage: Part I,” J. Bone Jt. Surg. 79(4), 600 (1997).
[Crossref]

JBJS (1)

H. J. Mankin, M. E. Johnson, and L. Lippiello, “Biochemical and metabolic abnormalities in articular cartilage from osteoarthritic human hips. III. Distribution and metabolism of amino sugar-containing macromolecules,” JBJS 63(1), 131–139 (1981).
[Crossref]

Journal of biomechanics (3)

W. C. Hayes, L. M. Keer, G. Herrmann, and L. F. Mockros, “A mathematical analysis for indentation tests of articular cartilage,” Journal of biomechanics 5(5), 541–551 (1972).
[Crossref]

A. F. Mak, W. M. Lai, and V. C. Mow, “Biphasic indentation of articular cartilage—I. Theoretical analysis,” Journal of biomechanics 20(7), 703–714 (1987).
[Crossref]

J. S. Jurvelin, T. Räsänen, P. Kolmonens, and T. Lyyra, “Comparison of optical, needle probe and ultrasonic techniques for the measurement of articular cartilage thickness,” Journal of biomechanics 28(2), 231–235 (1995).
[Crossref]

Journal of the mechanical behaviour of biomedical material (1)

I. O. Afara, S. Singh, and A. Oloyede, “Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra,” Journal of the mechanical behaviour of biomedical material 20, 249–258 (2013).
[Crossref]

Lasers Med. Sci. (2)

L. O. Svaasand, L. T. Norvang, E. J. Fiskerstrand, E. K. S. Stopps, M. W. Berns, and J. S. Nelson, “Tissue parameters determining the visual appearance of normal skin and port-wine stains,” Lasers Med. Sci. 10(1), 55–65 (1995).
[Crossref]

T. Lister, P. Wright, and P. Chappell, “Spectrophotometers for the clinical assessment of port-wine stain skin lesions: a review,” Lasers Med. Sci. 25(3), 449–457 (2010).
[Crossref]

Matrix Biol. (1)

U. P Palukuru, C. M. McGoverin, and N. Pleshko, “Assessment of hyaline cartilage matrix composition using near infrared spectroscopy,” Matrix Biol. 38, 3–11 (2014).
[Crossref]

Medical Engineering Physics (2)

I. O. Afara, S. Singh, and A. Oloyede, “Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage,” Medical Engineering Physics 35(1), 88–95 (2013).
[Crossref]

G. Spahn, H. Plettenberg, H. Nagel, E. Kahl, H. M. Klinger, T. Mückley, M. Günther, G. O. Hofmann, and J. A. Mollenhauer, “Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study,” Medical Engineering Physics 30(3), 285–292 (2008).
[Crossref]

Nat. Protoc. (1)

I. O. Afara, R. Shaikh, E. Nippolainen, W. Querido, J. Torniainen, J. K. Sarin, S. Kandel, N. Pleshko, and J. Töyräs, “Characterization of connective tissues using near-infrared spectroscopy and imaging,” Nat. Protoc. 16(2), 1297–1329 (2021).
[Crossref]

Opt. Express (1)

Osteoarthritis and cartilage (7)

I. O. Afara, I. Prasadam, R. Crawford, Y. Xiao, and A. Oloyede, “Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score,” Osteoarthritis and cartilage 20(11), 1367–1373 (2012).
[Crossref]

P. H. Puhakka, N. C. R. Te Moller, I. O. Afara, J. T. A. Mäkelä, V. Tiitu, R. K. Korhonen, H. Brommer, T. Virén, J. S. Jurvelin, and J. Töyräs, “Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal,” Osteoarthritis and Cartilage 23(12), 2206–2213 (2015).
[Crossref]

M. Prakash, A. Joukainen, J. Torniainen, M. K. M. Honkanen, L. Rieppo, I. O. Afara, H. Kröger, J. Töyräs, and J. K. Sarin, “Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy,” Osteoarthritis and cartilage 27(8), 1235–1243 (2019).
[Crossref]

S. Saarakkala, P. Julkunen, P. Kiviranta, J. Mäkitalo, J. S. Jurvelin, and R. K. Korhonen, “Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics,” Osteoarthritis and Cartilage 18(1), 73–81 (2010).
[Crossref]

R. U. Kleemann, D. Krocker, A. Cedraro, J. Tuischer, and G. N. Duda, “Altered cartilage mechanics and histology in knee osteoarthritis: relation to clinical assessment (ICRS Grade),” Osteoarthritis and cartilage 13(11), 958–963 (2005).
[Crossref]

A. Thambyah, A. Nather, and J. Goh, “Mechanical properties of articular cartilage covered by the meniscus,” Osteoarthritis and Cartilage 14(6), 580–588 (2006).
[Crossref]

J. T. A. Mäkelä, Z. S. Rezaeian, S. Mikkonen, R. Madden, S. K. Han, J. S. Jurvelin, W. Herzog, and R. K. Korhonen, “Site-dependent changes in structure and function of lapine articular cartilage 4 weeks after anterior cruciate ligament transection,” Osteoarthritis and Cartilage 22(6), 869–878 (2014).
[Crossref]

Phys. Med. Biol. (1)

S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013).
[Crossref]

Physiol. Meas. (1)

I. O. Afara, M. Hauta-Kasari, J. S. Jurvelin, A. Oloyede, and J. Töyräs, “Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition,” Physiol. Meas. 36(9), 1913–1928 (2015).
[Crossref]

Sci. Rep. (2)

J. K. Sarin, L. Rieppo, H. Brommer, I. O. Afara, S. Saarakkala, and J. Töyräs, “Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure,” Sci. Rep. 7(1), 10586–9 (2017).
[Crossref]

I.O. Afara, I. Prasadam, Z. Arabshahi, Y. Xiao, and A. Oloyede, “Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy,” Sci. Rep. 7(1), 11463–9 (2017).
[Crossref]

Skeletal Radiol (1)

S. Nebelung, N. Brill, M. Tingart, T. Pufe, C. Kuhl, H. Jahr, and D. Truhn, “Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration,” Skeletal Radiol 45(4), 505–516 (2016).
[Crossref]

Sports Health (1)

A. J. Sophia Fox, A. Bedi, and S.A. Rodeo, “The basic science of articular cartilage: structure, composition, and function,” Sports Health 1(6), 461–468 (2009).
[Crossref]

Tissue Eng., Part B (1)

T. J. Klein, J. Malda, R. L. Sah, and D. W. Hutmacher, “Tissue engineering of articular cartilage with biomimetic zones,” Tissue Eng., Part B 15(2), 143–157 (2009).
[Crossref]

Vet. J. (1)

N. C. R. Te Moller, H. Brommer, J. Liukkonen, T. Virén, M. Timonen, P. H. Puhakka, J. S. Jurvelin, P. R. Van Weeren, and J. Töyräs, “Arthroscopic optical coherence tomography provides detailed information on articular cartilage lesions in horses,” Vet. J. 197(3), 589–595 (2013).
[Crossref]

Other (5)

WHO, Chronic Rheumatic Conditions. (2014).

M. Maldonado and J. Nam, “The role of changes in extracellular matrix of cartilage in the presence of inflammation on the pathology of osteoarthritis,” Biomed Research International, 2013.

H. C. Van de Hulst, Multiple Light Scattering: Tables, Formulas, and Applications (Elsevier, 2012).

S. Chandrasekhar, Radiative Transfer (Dover Publications, 2013).

L. Wang and S. L. Jacques. “Monte Carlo modeling of light transport in multi-layered tissues in standard C,” The University of Texas, MD Anderson Cancer Center, Houston 4(11), (1992).

Supplementary Material (1)

NameDescription
Supplement 1       Figures 4,5,6(c) with extended Y-axis

Data availability

The concerned datasets and codes regarding results presented in this paper can be provided upon request from the authors.

Cited By

Optica participates in Crossref's Cited-By Linking service. Citing articles from Optica Publishing Group journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (6)

Fig. 1.
Fig. 1. (a) Locations for sample extraction (b) Depth-wise cutting in layers for optical measurement.
Fig. 2.
Fig. 2. (a,b) Absorption and reduced scattering coefficients (µa and µ’s) of articular cartilage samples in healthy group along with standard error of mean (SEM).
Fig. 3.
Fig. 3. (a,b)$\; $Absorption and reduced scattering coefficients (µa and µ’s) of articular cartilage samples in degenerated group including SEM.
Fig. 4.
Fig. 4. (a,b) Statistical significance (t-test) for ${\mu _a}$ and $\mu _s^{\prime}$ between zones of articular cartilage samples in healthy group (c,d) Statistical significance (t-test) for ${\mu _a}$, and $\mu _s^{\prime}$ between zones of articular cartilage samples in degenerated group.
Fig. 5.
Fig. 5. (a) Statistical significance (t-test) for ${\mu _a}$ between the zones of articular cartilage samples in healthy and degenerated groups (b) Statistical significance (t-test) for $\mu _s^{\prime}$ between the zones of articular cartilage samples in healthy and degenerated groups.
Fig. 6.
Fig. 6. (a) Absorption coefficient (µa) of full thickness normal and degenerated tissue (b) Reduced scattering coefficient ($\mu _s^{\prime}$) of full thickness normal and degenerated tissue (c) Statistical significance (t-test) for reduced scattering coefficient ($\mu _s^{\prime}$) between full thickness normal and degenerated tissue (d) Penetration depth of NIR photons into the healthy and degenerated cartilage

Equations (1)

Equations on this page are rendered with MathJax. Learn more.

P D = 1 3 μ a ( μ a + μ s )