Classification of degraded cartilage through multiparametric MRI analysis

Machine Learning Prediction of Collagen Fiber Orientation and Proteoglycan Content From Multiparametric Quantitative MRI in Articular Cartilage

BACKGROUND

Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage.

PURPOSE

To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content.

STUDY TYPE

Retrospective, animal model.

ANIMAL MODEL

An open-source single slice MRI dataset obtained from 20 samples of 10 Shetland ponies (seven with surgically induced cartilage lesions followed by treatment and three healthy controls) yielded to 1600 data points, including 10% for test and 90% for train validation.

FIELD STRENGTH/SEQUENCE

A 9.4 T MRI scanner/qMRI sequences: T₁ , T₂ , adiabatic T_1ρ and T_2ρ , continuous-wave T_1ρ and relaxation along a fictitious field (T_RAFF ) maps.

ASSESSMENT

Five machine learning regression models were developed: random forest (RF), support vector regression (SVR), gradient boosting (GB), multilayer perceptron (MLP), and Gaussian process regression (GPR). A nested cross-validation was used for performance evaluation. For reference, proteoglycan content and collagen fiber orientation were determined by quantitative histology from digital densitometry (DD) and polarized light microscopy (PLM), respectively.

STATISTICAL TESTS

Normality was tested using Shapiro-Wilk test, and association between predicted and measured values was evaluated using Spearman's Rho test. A P-value of 0.05 was considered as the limit of statistical significance.

RESULTS

Four out of the five models (RF, GB, MLP, and GPR) yielded high accuracy (R²  = 0.68-0.75 for PLM and 0.62-0.66 for DD), and strong significant correlations between the reference measurements and predicted cartilage matrix properties (Spearman's Rho = 0.72-0.88 for PLM and 0.61-0.83 for DD). GPR algorithm had the highest accuracy (R²  = 0.75 and 0.66) and lowest prediction-error (root mean squared [RMSE] = 1.34 and 2.55) for PLM and DD, respectively.

DATA CONCLUSION

Multiparametric qMRIs in combination with regression models can determine cartilage compositional and structural features, with higher accuracy for collagen fiber orientation than proteoglycan content.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Evaluation of articular cartilage with quantitative MRI in an equine model of post-traumatic osteoarthritis

Chondral lesions lead to degenerative changes in the surrounding cartilage tissue, increasing the risk of developing post-traumatic osteoarthritis (PTOA). This study aimed to investigate the feasibility of quantitative magnetic resonance imaging (qMRI) for evaluation of articular cartilage in PTOA. Articular explants containing surgically induced and repaired chondral lesions were obtained from the stifle joints of seven Shetland ponies (14 samples). Three age-matched nonoperated ponies served as controls (six samples). The samples were imaged at 9.4 T. The measured qMRI parameters included T₁ , T₂ , continuous-wave T_1ρ (CWT_1ρ ), adiabatic T_1ρ (AdT_1ρ ), and T_2ρ (AdT_2ρ ) and relaxation along a fictitious field (T_RAFF ). For reference, cartilage equilibrium and dynamic moduli, proteoglycan content and collagen fiber orientation were determined. Mean values and profiles from full-thickness cartilage regions of interest, at increasing distances from the lesions, were used to compare experimental against control and to correlate qMRI with the references. Significant alterations were detected by qMRI parameters, including prolonged T₁ , CWT_1ρ , and AdT_1ρ in the regions adjacent to the lesions. The changes were confirmed by the reference methods. CWT_1ρ was more strongly associated with the reference measurements and prolonged in the affected regions at lower spin-locking amplitudes. Moderate to strong correlations were found between all qMRI parameters and the reference parameters (ρ = -0.531 to -0.757). T₁ , low spin-lock amplitude CWT_1ρ , and AdT_1ρ were most responsive to changes in visually intact cartilage adjacent to the lesions. In the context of PTOA, these findings highlight the potential of T₁ , CWT_1ρ , and AdT_1ρ in evaluation of compositional and structural changes in cartilage.

Functional MRI for evaluation of hyaline cartilage extracelullar matrix, a physiopathological-based approach

MRI of articular cartilage (AC) integrity has potential to become a biomarker for osteoarthritis progression. Traditional MRI sequences evaluate AC morphology, allowing for the measurement of thickness and its change over time. In the last two decades, more advanced, dedicated MRI cartilage sequences have been developed aiming to assess AC matrix composition non-invasively and detect early changes in cartilage not captured on morphological sequences. T2-mapping and T1ρ sequences can be used to estimate the relaxation times of water inside the AC. These sequences have been introduced into clinical protocols and show promising results for cartilage assessment. Extracelullar matrix can also be assessed using diffusion-weighted imaging and diffusion tensor imaging as the movement of water is limited by the presence of extracellular matrix in AC. Specific techniques for glycosaminoglycans (GAG) evaluation, such as delayed gadolinium enhanced MRI of cartilage or Chemical Exchange Saturation Transfer imaging of GAG, as well as sodium imaging have also shown utility in the detection of AC damage. This manuscript provides an educational update on the physical principles behind advanced AC MRI techniques as well as a comprehensive review of the strengths and weaknesses of each approach. Current clinical applications and potential future applications of these techniques are also discussed.

Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative

The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty-eight subjects were selected from the osteoarthritis initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire 3 years after baseline evaluation. Multi-slice T2 -weighted knee images, obtained through the OAI, of these subjects were registered using a nonlinear image registration algorithm. T2 maps of cartilage from the central weight bearing slices of the medial femoral condyle were derived from the registered images using the multiple available echo times and were classified for "progression to symptomatic OA" using the machine learning tool, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). WND-CHRM classified the isolated T2 maps for the progression to symptomatic OA with 75% accuracy.

CLINICAL SIGNIFICANCE

Machine learning algorithms applied to T2 maps have the potential to provide important prognostic information for the development of OA. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2243-2250, 2017.

MR Parametric Mapping as a Biomarker of Early Joint Degeneration

Context:

Osteoarthritis (OA) is a common, worldwide disorder. Magnetic resonance (MR) imaging can directly and noninvasively evaluate articular cartilage and has emerged as an essential tool in the study of OA.

Evidence Acquisition:

A PubMed search was performed using the keywords quantitative MRI and cartilage. No limits were set on the range of years searched. Articles were reviewed for relevance with an emphasis on in vivo studies performed at 3 tesla.

Study Design:

Clinical review.

Level of Evidence:

Level 4.

Results:

T2, T2*, T1 (particularly when measured after exogenous contrast administration, such as with the delayed gadolinium-enhanced MR imaging of cartilage [dGEMRIC] technique), and T1ρ are among the most widely utilized quantitative MR imaging techniques to evaluate cartilage and have been implemented in various patient cohorts. Existing challenges include reproducibility of results, insufficient consensus regarding optimal sequences and parameters, and interpretation of values.

Conclusion:

Quantitative assessment of cartilage using MR imaging techniques likely represents the best opportunity to identify early cartilage degeneration and to follow patients after treatment. Despite existing challenges, ongoing work and unique approaches have shown exciting and promising results.

Multiparametric Classification of Skin from Osteogenesis Imperfecta Patients and Controls by Quantitative Magnetic Resonance Microimaging

The purpose of this study is to evaluate the ability of quantitative magnetic resonance imaging (MRI) to discriminate between skin biopsies from individuals with osteogenesis imperfecta (OI) and skin biopsies from individuals without OI. Skin biopsies from nine controls (unaffected) and nine OI patients were imaged to generate maps of five separate MR parameters, T₁, T₂, k_m, MTR and ADC. Parameter values were calculated over the dermal region and used for univariate and multiparametric classification analysis. A substantial degree of overlap of individual MR parameters was observed between control and OI groups, which limited the sensitivity and specificity of univariate classification. Classification accuracies ranging between 39% and 67% were found depending on the variable of investigation, with T₂ yielding the best accuracy of 67%. When several MR parameters were considered simultaneously in a multivariate analysis, the classification accuracies improved up to 89% for specific combinations, including the combination of T₂ and k_m. These results indicate that multiparametric classification by quantitative MRI is able to detect differences between the skin of OI patients and of unaffected individuals, which motivates further study of quantitative MRI for the clinical diagnosis of OI.

Multi-parametric MRI characterization of enzymatically degraded articular cartilage

Several laboratory and rotating frame quantitative MRI parameters were evaluated and compared for detection of changes in articular cartilage following selective enzymatic digestion. Bovine osteochondral specimens were subjected to 44 h incubation in control medium or in collagenase or chondroitinase ABC to induce superficial collagen or proteoglycan (glycosaminoglycan) alterations. The samples were scanned at 9.4 T for T1 , T1 Gd (dGEMRIC), T2 , adiabatic T1 ρ , adiabatic T2 ρ , continuous-wave T1 ρ , TRAFF2 , and T1 sat relaxation times and for magnetization transfer ratio (MTR). For reference, glycosaminoglycan content, collagen fibril orientation and biomechanical properties were determined. Changes primarily in the superficial cartilage were noted after enzymatic degradation. Most of the studied parameters were sensitive to the destruction of collagen network, whereas glycosaminoglycan depletion was detected only by native T1 and T1 Gd relaxation time constants throughout the tissue and by MTR superficially. T1 , adiabatic T1 ρ , adiabatic T2 ρ , continuous-wave T1 ρ , and T1 sat correlated significantly with the biomechanical properties while T1 Gd correlated with glycosaminoglycan staining. The findings indicated that most of the studied MRI parameters were sensitive to both glycosaminoglycan content and collagen network integrity, with changes due to enzymatic treatment detected primarily in the superficial tissue. Strong correlation of T1 , adiabatic T1ρ , adiabatic T2 ρ , continuous-wave T1 ρ , and T1 sat with the altered biomechanical properties, reflects that these parameters were sensitive to critical functional properties of cartilage. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 34:1111-1120, 2016.

Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

OBJECTIVE

The purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions.

METHOD

An approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the Osteoarthritis Research Society International (OARSI) histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug.

RESULTS

The binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values.

CONCLUSION

MRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis.

Classification of histologically scored human knee osteochondral plugs by quantitative analysis of magnetic resonance images at 3T

This work evaluates the ability of quantitative MRI to discriminate between normal and pathological human osteochondral plugs characterized by the Osteoarthritis Research Society International (OARSI) histological system. Normal and osteoarthritic human osteochondral plugs were scored using the OARSI histological system and imaged at 3 T using MRI sequences producing T1 and T2 contrast and measuring T1, T2, and T2* relaxation times, magnetization transfer, and diffusion. The classification accuracies of quantitative MRI parameters and corresponding weighted image intensities were evaluated. Classification models based on the Mahalanobis distance metric for each MRI measurement were trained and validated using leave-one-out cross-validation with plugs grouped according to OARSI histological grade and score. MRI measurements used for classification were performed using a region-of-interest analysis which included superficial, deep, and full-thickness cartilage. The best classifiers based on OARSI grade and score were T1- and T2-weighted image intensities, which yielded accuracies of 0.68 and 0.75, respectively. Classification accuracies using OARSI score-based group membership were generally higher when compared with grade-based group membership. MRI-based classification--either using quantitative MRI parameters or weighted image intensities--is able to detect early osteoarthritic tissue changes as classified by the OARSI histological system. These findings suggest the benefit of incorporating quantitative MRI acquisitions in a comprehensive clinical evaluation of OA.

Techniques and applications of in vivo diffusion imaging of articular cartilage

Early in the process of osteoarthritis (OA) the composition (water, proteoglycan [PG], and collagen) and structure of articular cartilage is altered leading to changes in its mechanical properties. A technique that can assess the composition and structure of the cartilage in vivo can provide insight in the mechanical integrity of articular cartilage and become a powerful tool for the early diagnosis of OA. Diffusion tensor imaging (DTI) has been proposed as a biomarker for cartilage composition and structure. DTI is sensitive to the PG content through the mean diffusivity and to the collagen architecture through the fractional anisotropy. However, the acquisition of DTI of articular cartilage in vivo is challenging due to the short T2 of articular cartilage (∼40 ms at 3 Tesla) and the high resolution needed (0.5-0.7 mm in plane) to depict the cartilage anatomy. We describe the pulse sequences used for in vivo DTI of articular cartilage and discus general strategies for protocol optimization. We provide a comprehensive review of measurements of DTI of articular cartilage from ex vivo validation experiments to its recent clinical applications.

Classification of sodium MRI data of cartilage using machine learning

PURPOSE

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested.

METHODS

Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification.

RESULTS

Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes.

CONCLUSION

Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data.

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions

BACKGROUND

To evaluate the sensitivity and specificity of classification of pathomimetically degraded bovine nasal cartilage at 3 Tesla and 37°C using univariate MRI measurements of both pure parameter values and intensities of parameter-weighted images.

METHODS

Pre- and posttrypsin degradation values of T1 , T2 , T2 *, magnetization transfer ratio (MTR), and apparent diffusion coefficient (ADC), and corresponding weighted images, were analyzed. Classification based on the Euclidean distance was performed and the quality of classification was assessed through sensitivity, specificity and accuracy (ACC).

RESULTS

The classifiers with the highest accuracy values were ADC (ACC = 0.82 ± 0.06), MTR (ACC = 0.78 ± 0.06), T1 (ACC = 0.99 ± 0.01), T2 derived from a three-dimensional (3D) spin-echo sequence (ACC = 0.74 ± 0.05), and T2 derived from a 2D spin-echo sequence (ACC = 0.77 ± 0.06), along with two of the diffusion-weighted signal intensities (b = 333 s/mm(2) : ACC = 0.80 ± 0.05; b = 666 s/mm(2) : ACC = 0.85 ± 0.04). In particular, T1 values differed substantially between the groups, resulting in atypically high classification accuracy. The second-best classifier, diffusion weighting with b = 666 s/mm(2) , as well as all other parameters evaluated, exhibited substantial overlap between pre- and postdegradation groups, resulting in decreased accuracies.

CONCLUSION

Classification according to T1 values showed excellent test characteristics (ACC = 0.99), with several other parameters also showing reasonable performance (ACC > 0.70). Of these, diffusion weighting is particularly promising as a potentially practical clinical modality. As in previous work, we again find that highly statistically significant group mean differences do not necessarily translate into accurate clinical classification rules.

Functional imaging in OA: role of imaging in the evaluation of tissue biomechanics

Functional imaging refers broadly to the visualization of organ or tissue physiology using medical image modalities. In load-bearing tissues of the body, including articular cartilage lining the bony ends of joints, changes in strain, stress, and material properties occur in osteoarthritis (OA), providing an opportunity to probe tissue function through the progression of the disease. Here, biomechanical measures in cartilage and related joint tissues are discussed as key imaging biomarkers in the evaluation of OA. Emphasis will be placed on the (1) potential of radiography, ultrasound, and magnetic resonance imaging to assess early tissue pathomechanics in OA, (2) relative utility of kinematic, structural, morphological, and biomechanical measures as functional imaging biomarkers, and (3) improved diagnostic specificity through the combination of multiple imaging biomarkers with unique contrasts, including elastography and quantitative assessments of tissue biochemistry. In comparison to other modalities, magnetic resonance imaging provides an extensive range of functional measures at the tissue level, with conventional and emerging techniques available to potentially to assess the spectrum of preclinical to advance OA.

Assessment of hyaline cartilage matrix composition using near infrared spectroscopy

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

Multiparametric MRI assessment of human articular cartilage degeneration: Correlation with quantitative histology and mechanical properties

PURPOSE

To evaluate the sensitivity of quantitative MRI techniques (T₁ , T_1,Gd , T₂ , continous wave (CW) T_1ρ dispersion, adiabatic T_1ρ , adiabatic T_2ρ , RAFF and inversion-prepared magnetization transfer (MT)) for assessment of human articular cartilage with varying degrees of natural degeneration.

METHODS

Osteochondral samples (n = 14) were obtained from the tibial plateaus of patients undergoing total knee replacement. MRI of the specimens was performed at 9.4T and the relaxation time maps were evaluated in the cartilage zones. For reference, quantitative histology, OARSI grading and biomechanical measurements were performed and correlated with MRI findings.

RESULTS

All MRI parameters, except T_1,Gd , showed statistically significant differences in tangential and full-thickness regions of interest (ROIs) between early and advanced osteoarthritis (OA) groups, as classified by OARSI grading. CW-T_1ρ showed significant dispersion in all ROIs and featured classical laminar structure of cartilage with spin-lock powers below 1000 Hz. Adiabatic T_1ρ , T_2ρ , CW-T_1ρ, MT, and RAFF correlated strongly with OARSI grade and biomechanical parameters.

CONCLUSION

MRI parameters were able to differentiate between early and advanced OA. Furthermore, rotating frame methods, namely adiabatic T_1ρ , adiabatic T_2ρ , CW-T_1ρ , and RAFF, as well as MT experiment correlated strongly with biomechanical parameters and OARSI grade, suggesting high sensitivity of the parameters for cartilage degeneration. Magn Reson Med 74:249-259, 2015. © 2014 Wiley Periodicals, Inc.

Fourier transform infrared spectroscopy to quantify collagen and elastin in an in vitro model of extracellular matrix degradation in aorta

Extracellular matrix (ECM) is a key component and regulator of many biological tissues including aorta. Several aortic pathologies are associated with significant changes in the composition of the matrix, especially in the content, quality and type of aortic structural proteins, collagen and elastin. The purpose of this study was to develop an infrared spectroscopic methodology that is comparable to biochemical assays to quantify collagen and elastin in aorta. Enzymatically degraded porcine aorta samples were used as a model of ECM degradation in abdominal aortic aneurysm (AAA). After enzymatic treatment, Fourier transform infrared (FTIR) spectra of the aortic tissue were acquired by an infrared fiber optic probe (IFOP) and FTIR imaging spectroscopy (FT-IRIS). Collagen and elastin content were quantified biochemically and partial least squares (PLS) models were developed to predict collagen and elastin content in aorta based on FTIR spectra. PLS models developed from FT-IRIS spectra were able to predict elastin and collagen content of the samples with strong correlations (RMSE of validation = 8.4% and 11.1% of the range respectively), and IFOP spectra were successfully used to predict elastin content (RMSE = 11.3% of the range). The PLS regression coefficients from the FT-IRIS models were used to map collagen and elastin in tissue sections of degraded porcine aortic tissue as well as a human AAA biopsy tissue, creating a similar map of each component compared to histology. These results support further application of FTIR spectroscopic techniques for evaluation of AAA tissues.

NMR dispersion investigations of enzymatically degraded bovine articular cartilage

PURPOSE

Cross-relaxation of protons with (14) N nuclei in proteins enhances relaxivity in the quadrupolar dip range of typically 2-3 MHz Larmor frequency. The magnitude of these dips was suggested as a means of assessing the degeneracy of articular cartilage during osteoarthritis (OA). However, so far only proteoglycans have been considered whereas collagen nitrogen was neglected. This study addresses the relative importance of glycosaminoglycans (GAG), collagen, and water content for the cross-relaxation effect.

METHODS

Relaxation dispersion data were acquired for protons in samples of bovine articular cartilage, collagen, and GAG before and after the addition of trypsin or collagenase, and were compared with spatially resolved dGEMRIC experiments at 0.27 Tesla.

RESULTS

Both collagen as well as GAG show quadrupolar dips that strongly depend on hydration. For typical water concentrations in cartilage, the effect of enzymatic activity onto GAG is minor but a strong dependence on water concentration is found.

CONCLUSION

Quadrupolar dips in the (1) H relaxation dispersion of cartilage possess similar contributions from both GAG and collagen. The reduction of the cross-relaxation contribution observed in OA tissue is thus not directly proportional to GAG concentration, but maintains a collagen contribution and reflects predominantly the increase in water concentration during OA.

Prediction of cartilage compressive modulus using multiexponential analysis of T(2) relaxation data and support vector regression

Evaluation of mechanical characteristics of cartilage by magnetic resonance imaging would provide a noninvasive measure of tissue quality both for tissue engineering and when monitoring clinical response to therapeutic interventions for cartilage degradation. We use results from multiexponential transverse relaxation analysis to predict equilibrium and dynamic stiffness of control and degraded bovine nasal cartilage, a biochemical model for articular cartilage. Sulfated glycosaminoglycan concentration/wet weight (ww) and equilibrium and dynamic stiffness decreased with degradation from 103.6 ± 37.0 µg/mg ww, 1.71 ± 1.10 MPa and 15.3 ± 6.7 MPa in controls to 8.25 ± 2.4 µg/mg ww, 0.015 ± 0.006 MPa and 0.89 ± 0.25MPa, respectively, in severely degraded explants. Magnetic resonance measurements were performed on cartilage explants at 4 °C in a 9.4 T wide-bore NMR spectrometer using a Carr-Purcell-Meiboom-Gill sequence. Multiexponential T2 analysis revealed four water compartments with T2 values of approximately 0.14, 3, 40 and 150 ms, with corresponding weight fractions of approximately 3, 2, 4 and 91%. Correlations between weight fractions and stiffness based on conventional univariate and multiple linear regressions exhibited a maximum r(2) of 0.65, while those based on support vector regression (SVR) had a maximum r(2) value of 0.90. These results indicate that (i) compartment weight fractions derived from multiexponential analysis reflect cartilage stiffness and (ii) SVR-based multivariate regression exhibits greatly improved accuracy in predicting mechanical properties as compared with conventional regression.

MRI rotating frame relaxation measurements for articular cartilage assessment

In the present work we introduced two MRI rotating frame relaxation methods, namely adiabatic T1ρ and Relaxation Along a Fictitious Field (RAFF), along with an inversion-prepared Magnetization Transfer (MT) protocol for assessment of articular cartilage. Given the inherent sensitivity of rotating frame relaxation methods to slow molecular motions that are relevant in cartilage, we hypothesized that adiabatic T1ρ and RAFF would have higher sensitivity to articular cartilage degradation as compared to laboratory frame T2 and MT. To test this hypothesis, a proteoglycan depletion model was used. Relaxation time measurements were performed at 0 and 48h in 10 bovine patellar specimens, 5 of which were treated with trypsin and 5 untreated controls were stored under identical conditions in isotonic saline for 48h. Relaxation times measured at 48h were longer than those measured at 0h in both groups. The changes in T2 and MT relaxation times after 48h were approximately 3 times larger in the trypsin treated specimens as compared to the untreated group, whereas increases of adiabatic T1ρ and RAFF were 4 to 5 fold larger. Overall, these findings demonstrate a higher sensitivity of adiabatic T1ρ and RAFF to the trypsin-induced changes in bovine patellar cartilage as compared to the commonly used T2 and MT. Since adiabatic T1ρ and RAFF are advantageous for human applications as compared to standard continuous-wave T1ρ methods, adiabatic T1ρ and RAFF are promising tools for assessing cartilage degradation in clinical settings.

Near infrared spectroscopic evaluation of water in hyaline cartilage

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

Diffusion-tensor imaging of human articular cartilage specimens with early signs of cartilage damage

PURPOSE

To assess the use of diffusion-tensor (DT) imaging of articular cartilage to detect and grade early cartilage damage in human specimens with early signs of cartilage damage.

MATERIALS AND METHODS

This study was approved by the institutional review board. Forty-three cartilage-on-bone samples drilled from 21 human patellae were examined with 17.6-T magnetic resonance (MR) imaging and a diffusion-weighted spin-echo sequence (spatial resolution, 50 × 100 × 800 μm). Subsequently, samples underwent histologic analysis with safranin O staining. Cartilage damage on safranin O histologic slides was quantified with Osteoarthritis Research Society International (OARSI) grades; grades ranged from 0 (healthy) to 6 (bone remodeling). Maps of longitudinal diffusivity (λ(l)), transverse diffusivity (λ(t)), mean diffusivity (MD), and fractional anisotropy (FA) were calculated. Cartilage was segmented, and region of interest (ROI) analysis was performed and compared with histologic findings. Significant differences in MR parameters between the OARSI groups were assessed with the Tukey test. The value of DT imaging in the diagnosis and grading of cartilage damage was assessed with logistic regression analysis.

RESULTS

Samples had OARSI grades of 0 (n = 14), 1 (n = 11), 2 (n = 12), 3 (n = 4), and 4 (n = 2). Samples with an OARSI grade greater than 0 had significantly increased λ(l), λ(t), and MD (7%-25% increase) in the superficial cartilage growing deeper into cartilage with increasing OARSI grade. Samples with an OARSI grade greater than 0 showed significantly decreased FA in the deep cartilage (-25% to -35% decrease), suggesting that changes in the collagen architecture may occur early in cartilage degradation. DTI showed excellent performance in the detection of cartilage damage (accuracy, 0.95; 41 of 43 samples) and good performance in the grading of cartilage damage (accuracy, 0.74; 32 of 43 samples).

CONCLUSION

DT imaging of articular cartilage can enable physicians to detect and grade early cartilage damage.

Improved MR-based characterization of engineered cartilage using multiexponential T2 relaxation and multivariate analysis

Noninvasive monitoring of tissue quality would be of substantial use in the development of cartilage tissue engineering strategies. Conventional MR parameters provide noninvasive measures of biophysical tissue properties and are sensitive to changes in matrix development, but do not clearly distinguish between groups with different levels of matrix development. Furthermore, MR outcomes are nonspecific, with particular changes in matrix components resulting in changes in multiple MR parameters. To address these limitations, we present two new approaches for the evaluation of tissue engineered constructs using MR, and apply them to immature and mature engineered cartilage after 1 and 5 weeks of development, respectively. First, we applied multiexponential T(2) analysis for the quantification of matrix macromolecule-associated water compartments. Second, we applied multivariate support vector machine analysis using multiple MR parameters to improve detection of degree of matrix development. Monoexponential T(2) values decreased with maturation, but without further specificity. Much more specific information was provided by multiexponential analysis. The T(2) distribution in both immature and mature constructs was qualitatively comparable to that of native cartilage. The analysis showed that proteoglycan-bound water increased significantly during maturation, from a fraction of 0.05 ± 0.01 to 0.07 ± 0.01. Classification of samples based on individual MR parameters, T(1), T(2), k(m) or apparent diffusion coefficient, showed that the best classifiers were T(1) and k(m), with classification accuracies of 85% and 84%, respectively. Support vector machine analysis improved the accuracy to 98% using the combination (k(m), apparent diffusion coefficient). These approaches were validated using biochemical and Fourier transform infrared imaging spectroscopic analyses, which showed increased proteoglycan and collagen with maturation. In summary, multiexponential T(2) and multivariate support vector machine analyses provide improved sensitivity to changes in matrix development and specificity to matrix composition in tissue engineered cartilage. These approaches show substantial potential for the evaluation of engineered cartilage tissue and for extension to other tissue engineering constructs.

Characterization of engineered cartilage constructs using multiexponential T₂ relaxation analysis and support vector regression

Increased sensitivity in the characterization of cartilage matrix status by magnetic resonance (MR) imaging, through the identification of surrogate markers for tissue quality, would be of great use in the noninvasive evaluation of engineered cartilage. Recent advances in MR evaluation of cartilage include multiexponential and multiparametric analysis, which we now extend to engineered cartilage. We studied constructs which developed from chondrocytes seeded in collagen hydrogels. MR measurements of transverse relaxation times were performed on samples after 1, 2, 3, and 4 weeks of development. Corresponding biochemical measurements of sulfated glycosaminoglycan (sGAG) were also performed. sGAG per wet weight increased from 7.74±1.34 μg/mg in week 1 to 21.06±4.14 μg/mg in week 4. Using multiexponential T₂ analysis, we detected at least three distinct water compartments, with T₂ values and weight fractions of (45 ms, 3%), (200 ms, 4%), and (500 ms, 97%), respectively. These values are consistent with known properties of engineered cartilage and previous studies of native cartilage. Correlations between sGAG and MR measurements were examined using conventional univariate analysis with T₂ data from monoexponential fits with individual multiexponential compartment fractions and sums of these fractions, through multiple linear regression based on linear combinations of fractions, and, finally, with multivariate analysis using the support vector regression (SVR) formalism. The phenomenological relationship between T₂ from monoexponential fitting and sGAG exhibited a correlation coefficient of r²=0.56, comparable to the more physically motivated correlations between individual fractions or sums of fractions and sGAG; the correlation based on the sum of the two proteoglycan-associated fractions was r²=0.58. Correlations between measured sGAG and those calculated using standard linear regression were more modest, with r² in the range 0.43-0.54. However, correlations using SVR exhibited r² values in the range 0.68-0.93. These results indicate that the SVR-based multivariate approach was able to determine tissue sGAG with substantially higher accuracy than conventional monoexponential T₂ measurements or conventional regression modeling based on water fractions. This combined technique, in which the results of multiexponential analysis are examined with multivariate statistical techniques, holds the potential to greatly improve the accuracy of cartilage matrix characterization in engineered constructs using noninvasive MR data.

Correlation between apparent diffusion coefficient and viscoelasticity of articular cartilage in a porcine model

OBJECTIVE

Quantitative MR imaging techniques of degenerative cartilage have been reported as useful indicators of degenerative changes in cartilage extracellular matrix, which consists of proteoglycans, collagen, non-collagenous proteins, and water. Apparent diffusion coefficient (ADC) mapping of cartilage has been shown to correlate mainly with the water content of the cartilage. As the water content of the cartilage in turn correlates with its viscoelasticity, which directly affects the mechanical strength of articular cartilage, ADC can serve as a potentially useful indicator of the mechanical strength of cartilage. The aim of this study was to investigate the correlation between ADC and viscoelasticity as measured by indentation testing.

MATERIALS AND METHODS

Fresh porcine knee joints (n = 20, age 6 months) were obtained from a local abattoir. ADC of porcine knee cartilage was measured using a 3-Tesla MRI. Indentation testing was performed on an electromechanical precision-controlled system, and viscosity coefficient and relaxation time were measured as additional indicators of the viscoelasticity of cartilage. The relationship between ADC and viscosity coefficient as well as that between ADC and relaxation time were assessed.

RESULTS

ADC was correlated with relaxation time and viscosity coefficient (R(2) = 0.75 and 0.69, respectively, p < 0.01). The mean relaxation time values in the weight-bearing and non-weight-bearing regions were 0.61 ± 0.17 ms and 0.14 ± 0.08 ms, respectively.

CONCLUSIONS

This study found a moderate correlation between ADC and viscoelasticity in the superficial articular cartilage. Both molecular diffusion and viscoelasticity were higher in weight bearing than non-weight-bearing articular cartilage areas.

Multivariate analysis of cartilage degradation using the support vector machine algorithm

An important limitation in MRI studies of early osteoarthritis is that measured MRI parameters exhibit substantial overlap between different degrees of cartilage degradation. We investigated whether multivariate support vector machine analysis would permit improved tissue characterization. Bovine nasal cartilage samples were subjected to pathomimetic degradation and their T(1), T(2), magnetization transfer rate (k(m) ), and apparent diffusion coefficient (ADC) were measured. Support vector machine analysis performed using certain parameter combinations exhibited particularly favorable classification properties. The areas under the receiver operating characteristic (ROC) curve for detection of extensive and mild degradation were 1.00 and 0.94, respectively, using the set (T(1), k(m), ADC), compared with 0.97 and 0.60 using T(1), the best univariate classifier. Furthermore, a degradation probability for each sample, derived from the support vector machine formalism using the parameter set (T(1), k(m), ADC), demonstrated much stronger correlations (r(2) = 0.79-0.88) with direct measurements of tissue biochemical components than did even the best-performing individual MRI parameter, T(1) (r(2) = 0.53-0.64). These results, combined with our previous investigation of Gaussian cluster-based tissue discrimination, indicate that the combinations (T(1), k(m)) and (T(1), k(m), ADC) may emerge as particularly useful for characterization of early cartilage degradation.

Detection of osteoarthritis in knee and hip joints by fast field-cycling NMR

It is known that in the early stages of osteoarthritis, the concentration of glycan proteins decreases in articular cartilage. This phenomenon is under active research to develop a means to characterize osteoarthritis accurately in the early stages of the disease, when still reversible. However, no method of quantification has yet shown clear success in this area. In this article, we propose a novel approach to detect glycan depletion using fast field-cycling NMR. This technique was previously reported to allow noninvasive measurement of protein concentration via the (14)N quadrupolar relaxation in certain amide groups. We have demonstrated that the articular cartilage exhibits clear quadrupolar peaks that can be measured by a benchtop fast field-cycling NMR device and which changes significantly between normal and diseased tissues (P < 0.01). This signal is probably glycan specific. The method may have potential for early evaluation of osteoarthritis in patients on fast field-cycling-MRI scanners currently under evaluation in the authors' laboratory.

Anomalous NMR relaxation in cartilage matrix components and native cartilage: fractional-order models

We present a fractional-order extension of the Bloch equations to describe anomalous NMR relaxation phenomena (T(1) and T(2)). The model has solutions in the form of Mittag-Leffler and stretched exponential functions that generalize conventional exponential relaxation. Such functions have been shown by others to be useful for describing dielectric and viscoelastic relaxation in complex, heterogeneous materials. Here, we apply these fractional-order T(1) and T(2) relaxation models to experiments performed at 9.4 and 11.7 Tesla on type I collagen gels, chondroitin sulfate mixtures, and to bovine nasal cartilage (BNC), a largely isotropic and homogeneous form of cartilage. The results show that the fractional-order analysis captures important features of NMR relaxation that are typically described by multi-exponential decay models. We find that the T(2) relaxation of BNC can be described in a unique way by a single fractional-order parameter (α), in contrast to the lack of uniqueness of multi-exponential fits in the realistic setting of a finite signal-to-noise ratio. No anomalous behavior of T(1) was observed in BNC. In the single-component gels, for T(2) measurements, increasing the concentration of the largest components of cartilage matrix, collagen and chondroitin sulfate, results in a decrease in α, reflecting a more restricted aqueous environment. The quality of the curve fits obtained using Mittag-Leffler and stretched exponential functions are in some cases superior to those obtained using mono- and bi-exponential models. In both gels and BNC, α appears to account for micro-structural complexity in the setting of an altered distribution of relaxation times. This work suggests the utility of fractional-order models to describe T(2) NMR relaxation processes in biological tissues.

Mapping proteoglycan-bound water in cartilage: Improved specificity of matrix assessment using multiexponential transverse relaxation analysis

Association of MR parameters with cartilage matrix components remains an area of ongoing investigation. Multiexponential analysis of nonlocalized transverse relaxation data has previously been used to quantify water compartments associated with matrix macromolecules in cartilage. We extend this to mapping the proteoglycan (PG)-bound water fraction in cartilage, using mature and young bovine nasal cartilage model systems, toward the goal of matrix component-specific imaging. PG-bound water fraction from mature and young bovine nasal cartilage was 0.31 ± 0.04 and 0.22 ± 0.06, respectively, in agreement with biochemically derived PG content and PG-to-water weight ratios. Fourier transform infrared imaging spectroscopic-derived PG maps normalized by water content (IR-PG(ww) ) showed spatial correspondence with PG-bound water fraction maps. Extensive simulation analysis demonstrated that the accuracy and precision of our determination of PG-bound water fraction was within 2%, which is well-within the observed tissue differences. Our results demonstrate the feasibility of performing imaging-based multiexponential analysis of transverse relaxation data to map PG in cartilage.