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.

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.

Magnetic resonance studies of macromolecular content in engineered cartilage treated with pulsed low-intensity ultrasound.

Noninvasive monitoring of matrix development in tissue-engineered cartilage constructs would permit ongoing assessment with the ability to modify culture conditions during development to optimize tissue characteristics. In this study, chondrocytes seeded in a collagen hydrogel were exposed for 20 min/day to pulsed low-intensity ultrasound (PLIUS) at 30 mWcm(-2) and cultured for up to 5 weeks. Biochemical assays, histology, immunohistochemistry, Fourier transform infrared spectroscopy, and magnetic resonance imaging (MRI) were performed at weeks 3 and 5 after initiation of growth. The noninvasive MRI measurements were correlated with those from the invasive studies. In particular, MRI transverse relaxation time (T2) and magnetization transfer rate (k(m)) correlated with macromolecular content, which was increased by application of PLIUS. This indicates the sensitivity of MR techniques to PLIUS-induced changes in matrix development, and highlights the potential for noninvasive assessment of the efficacy of anabolic interventions for engineered tissue.