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

Abstract

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 cleanly distinguish between groups with different levels of matrix development. Furthermore, MR outcomes are nonspecific, with specific changes in matrix components resulting in changes in multiple MR parameters. To address these limitations, we present two new approaches for evaluation of tissue engineered constructs using MR, and apply them to immature and mature engineered cartilage after 1 week and 5 weeks of development, respectively. First, we applied multiexponential T2 analysis for quantification of matrix macromolecule-associated water compartments. Second, we applied multivariate support vector machine (SVM) analysis using multiple MR parameters to improve detection of degree of matrix development. Classification of samples based on individual MR parameters, T1, T2, km, or ADC, showed that the best classifiers were T1 and km, with classification accuracies of 85% and 84%, respectively. SVM analysis improved accuracy to 98% using the combination (km, ADC). These approaches were validated using biochemical and Fourier transform infrared imaging spectroscopy analyses, which showed increased proteoglycan and collagen with maturation. 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 comparable to that of native cartilage. The analysis also showed that proteoglycan-bound water increased significantly during maturation, from a fraction of 0.05±0.01 to 0.07±0.01. In summary, multivariate SVM and multiexponential T2 analysis provide improved sensitivity to changes in matrix development and specificity to matrix composition in tissue engineered cartilage. These approaches show substantial potential for evaluation of engineered cartilage tissue as well as extension to other tissue engineering constructs.

Reduction of water diffusion coefficient with increased engineered cartilage matrix growth observed using MRI

“…This is important in order (a) to assess the efficacy of tissue engineering approaches at an early growth stage, (b) for providing an accurate assessment of engineered tissue growth postimplantation, and (c) for monitoring the healing of cartilage after treatment. Magnetic resonance imaging (MRI) is a leading non-invasive characterization technique for monitoring growth in cartilage tissue engineering [2][3][4][5][6][7]. Research Cartilage tissue in its native state contains chondrocytes (~1%) imbedded in an extracellular matrix (ECM).…”

CHAPTER 14. Applications of MRI and MRS in Cartilage Therapeutics and Tissue Engineering

“…Reprinted with permission from Lin et al 112 The SVM approach has been extended to classification of maturation stage in engineered cartilage grown from bovine chondrocytes using a PGA scaffold. 111 The best univariate classifier for maturation, k m , showed a validation set classification accuracy of 0.85. In contrast, the best bivariate classifier in these engineered cartilage constructs was (k m , ADC), with an accuracy of 0.98.…”

“…110 Similar findings were obtained in the univariate analysis of engineered cartilage. 111 A number of multivariate approaches have been applied to MRI indices of cartilage matrix in an effort to define tissue status with greater sensitivity and specificity. 112,113 Similar to multiexponential T 2 analysis, this approach was initially applied to normal and enzymatically degraded cartilage.…”

Monitoring Cartilage Tissue Engineering Using Magnetic Resonance Spectroscopy, Imaging, and Elastography

“…It is likely that during engineered tissue growth, both the T 1 and T 2 relaxation times would decrease due to accumulation of both proteoglycans and collagen. It is possible to separate the contributions of water, proteoglycans and collagen content to the observed T 2 data of engineered cartilage using multiexponential analysis, 38,39,94 but such experiments require a very high SNR, hence, use of signals from the entire sample, not from individual image pixels. Nevertheless, using this technique, Reiter et al have found that the proteoglycan component in cartilage tissue taken from young and mature bovine nasal cartilage was highly correlated with T 2 and with the biochemical analysis.…”

“…MRS and imaging are wellestablished techniques that have been used to probe the structure and dynamics of biochemical changes in engineered tissues. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] MR elastography, while relatively new, is rapidly being employed to assess the mechanical properties of engineered tissues. 30,43 Using MR spectroscopy, imaging, and elastography together, it is possible to create three-dimensional (3D) maps of chemical shifts, relaxation times, diffusion coefficient, spectral couplings, and tissue stiffness.…”