Ping-Chang Lin

Multicomponent T2 relaxation analysis in cartilage.

MR techniques are sensitive to the early stages of osteoarthritis, characterized by disruption of collagen and loss of proteoglycan (PG), but are of limited specificity. Here, water compartments in normal and trypsin‐degraded bovine nasal cartilage were identified using a nonnegative least squares multiexponential analysis of T2 relaxation. Three components were detected: T2,1 = 2.3 ms, T2,2 = 25.2 ms, and T2,3 = 96.3 ms, with fractions w1 = 6.2%, w2 = 14.5%, and w3 = 79.3%, respectively. Trypsinization resulted in increased (P < 0.01) values of T2,2 = 64.2 ms and T2,3 = 149.4 ms, supporting their assignment to water compartments that are bound and loosely associated with PG, respectively. The T2 of the rapidly relaxing component was not altered by digestion, supporting assignment to relatively immobile collagen‐bound water. Relaxation data were simulated for a range of TE, number of echoes, and SNR to guide selection of acquisition parameters and assess the accuracy and precision of experimental results. Based on this, the expected experimental accuracy of measured T2s and associated weights was within 2% and 4% respectively, with precision within 1% and 3%. These results demonstrate the potential of multiexponential T2 analysis to increase the specificity of MR characterization of cartilage. Magn Reson Med, 2009.

Classification of degraded cartilage through multiparametric MRI analysis.

MRI analysis of cartilage matrix may play an important role in early detection and development of therapeutic protocols for degenerative joint disease. Correlations between MRI parameters and matrix integrity have been established in many studies, but the substantial overlap in values observed for normal and for degraded cartilage greatly limits the specificity of these analyses. We implemented established multiparametric analysis methods to define data clusters corresponding to control and degraded bovine nasal cartilage in two-, three-, and four-dimensional parameter spaces, and applied these results to discriminant analysis of a validation data set. Analyses were performed using the parameters (T(1), T(2), k(m), ADC), where k(m) is the magnetization transfer rate and ADC is the apparent diffusion coefficient. Results were compared to univariate analyses. Multiparametric k-means clustering led to no improvement over univariate analyses, with a maximum sensitivity and specificity in the range of 60-70% for the detection of degradation using T(1), and in the range of 80% sensitivity but only 36% specificity using the parameter pair (T(1), k(m)). In contrast, model-based analysis using more general Gaussian clusters resulted in markedly improved classification, with sensitivity and specificity reaching levels of 80-90% using the pair (T(1), k(m)). Finally, a fuzzy clustering technique was implemented which may be still more appropriate to the continuum of degradation seen in degenerative cartilage disease. In view of its success in identifying mild cartilage degradation, the formal multiparametric approach implemented here may be applicable to the nondestructive evaluation of other biomaterials using MRI.

Nondestructive Assessment of Engineered Cartilage Constructs Using Near-Infrared Spectroscopy

Noninvasive assessment of engineered cartilage properties would enable better control of the developing tissue towards the desired structural and compositional endpoints through optimization of the biochemical environment in real time. The objective of this study is to assess the matrix constituents of cartilage using near-infrared spectroscopy (NIRS), a technique that permits full-depth assessment of developing engineered tissue constructs. Mid-infrared (mid-IR) and NIR data were acquired from full-thickness cartilage constructs that were grown up to 4 weeks with and without mechanical stimulation. Correlations were assessed between established mid-IR peak areas that reflect the relative amount of collagen (amide I, amide II, and 1338 cm−1) and proteoglycan (PG), (850 cm−1), and the integrated area of the NIR water absorbance at 5190 cm−1. This analysis was performed to evaluate whether simple assessment of the NIR water absorbance could yield information about matrix development. It was found that an increase in the mid-IR PG absorbance at 850 cm−1 correlated with the area of the NIR water peak (Spearmans rho = 0.95, p < 0.0001). In the second analysis, a partial least squares method (PLS1) was used to assess whether an extended NIR spectral range (5400–3800 cm−1) could be utilized to predict collagen and proteoglycan content of the constructs based on mid-IR absorbances. A subset of spectra was randomly selected as an independent prediction set in this analysis. Average of the normalized root mean square errors of prediction of first-derivative NIR spectral models were 7% for 850 cm−1 (PG), 11% for 1338 cm−1 (collagen), 8% for amide II (collagen), and 8% for amide I (collagen). These results demonstrate the ability of NIRS to monitor macromolecular content of cartilage constructs and is the first step towards employing NIR to assess engineered cartilage in situ.

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‐PGww) 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. Magn Reson Med, 2011.

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 T2 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 T2 values decreased with maturation, but without further specificity. Much more specific information was provided by multiexponential analysis. The T2 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, T1, T2, km or apparent diffusion coefficient, showed that the best classifiers were T1 and km, with classification accuracies of 85% and 84%, respectively. Support vector machine analysis improved the accuracy to 98% using the combination (km, 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 T2 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. Copyright

Improved specificity of cartilage matrix evaluation using multiexponential transverse relaxation analysis applied to pathomimetically degraded cartilage.

The noninvasive early detection of specific matrix alterations in degenerative cartilage disease would be of substantial use in basic science studies and clinically, but remains an elusive goal. Recently developed MRI methods exhibit some specificity, but require contrast agents or nonstandard pulse sequences and hardware. We present a multiexponential approach which does not require contrast agents or specialized hardware, and uses a standard multiple‐echo spin‐echo sequence. Experiments were performed on tissue models of degenerative cartilage using enzymes with distinct actions. MR results were validated using histologic, biochemical and infrared spectroscopic analyses. The sulfated glycosaminoglycan per dry weight (dw) in bovine nasal cartilage was 0.72 ± 0.06 mg/mg dw and was reduced through chondroitinase AC and collagenase digestion to 0.56 ± 0.12 and 0.58 ± 0.13 mg/mg dw, respectively. Multiexponential analysis of data obtained at 9.4 T permitted the identification of tissue compartments assigned to the proteoglycan component of the matrix and to bulk water. Enzymatic treatment resulted in a significant reduction in the ratio of proteoglycan‐bound to free water from 0.13 ± 0.02 in control cartilage to 0.03 ± 0.02 and 0.05 ± 0.06 under chondroitinase AC and collagenase treatment, respectively. As expected, monoexponential T2 increased with both degradation protocols, but without further specificity to the nature of the degradation. An important eventual extension of this approach may be to map articular cartilage degeneration in the clinical setting. As an initial step towards this, localized multiexponential T2 analysis was performed on control and trypsin treated excised bovine patella. The results obtained on this articular cartilage sample were readily interpretable in terms of proteoglycan‐associated and relatively free water compartments. In potential clinical applications, signal‐to‐noise ratio constraints will define the threshold for the detection of macromolecular compartment changes at a given spatial scale. The multiexponential approach has potential application to the early detection of cartilage degradation with the use of appropriate pulse parameters under high signal‐to‐noise ratio conditions. Copyright

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage

MRI is increasingly used to evaluate cartilage in tissue constructs, explants, and animal and patient studies. However, while mean values of MR parameters, including T1, T2, magnetization transfer rate km, apparent diffusion coefficient (ADC), and the dGEMRIC‐derived fixed charge density, correlate with tissue status, the ability to classify tissue according to these parameters has not been explored. Therefore, the sensitivity and specificity with which each of these parameters was able to distinguish between normal and trypsin‐degraded, and between normal and collagenase‐degraded, cartilage explants were determined. Initial analysis was performed using a training set to determine simple group means to which parameters obtained from a validation set were compared. T1 and apparent diffusion coefficient showed the greatest ability to discriminate between normal and degraded cartilage. Further analysis with k‐means clustering, which eliminates the need for a priori identification of sample status, generally performed comparably. Use of fuzzy c‐means (FCM) clustering to define centroids likewise did not result in improvement in discrimination. Finally, an FCM clustering approach in which validation samples were assigned in a probabilistic fashion to control and degraded groups was implemented, reflecting the range of tissue characteristics seen with cartilage degradation. Magn Reson Med, 2009.

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 T1, T2, magnetization transfer rate (km), 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 (T1, km, ADC), compared with 0.97 and 0.60 using T1, the best univariate classifier. Furthermore, a degradation probability for each sample, derived from the support vector machine formalism using the parameter set (T1, km, ADC), demonstrated much stronger correlations (r2 = 0.79–0.88) with direct measurements of tissue biochemical components than did even the best‐performing individual MRI parameter, T1 (r2 = 0.53–0.64). These results, combined with our previous investigation of Gaussian cluster‐based tissue discrimination, indicate that the combinations (T1, km) and (T1, km, ADC) may emerge as particularly useful for characterization of early cartilage degradation. Magn Reson Med, 2011.

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, km, 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 km. 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.

Prediction of cartilage compressive modulus using multiexponential analysis of T2 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 r2 of 0.65, while those based on support vector regression (SVR) had a maximum r2 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. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.