Multiparametric MRI and Computational Modelling in the Assessment of Human Articular Cartilage Properties: A Comprehensive Approach.

Quantitative magnetic resonance imaging (qMRI) is a promising approach to detect early cartilage degeneration. However, there is no consensus on which cartilage component contributes to the tissue's qMRI signal properties. T1, T1ρ, and T2⁎ maps of cartilage samples (n = 8) were generated on a clinical 3.0-T MRI system. All samples underwent histological assessment to ensure structural integrity. For cross-referencing, a discretized numerical model capturing distinct compositional and structural tissue properties, that is, fluid fraction (FF), proteoglycan (PG) and collagen (CO) content and collagen fiber orientation (CFO), was implemented. In a pixel-wise and region-specific manner (central versus peripheral region), qMRI parameter values and modelled tissue parameters were correlated and quantified in terms of Spearman's correlation coefficient ρs. Significant correlations were found between modelled compositional parameters and T1 and T2⁎, in particular in the central region (T1: ρs ≥ 0.7 [FF, CFO], ρs ≤ -0.8 [CO, PG]; T2⁎: ρs ≥ 0.67 [FF, CFO], ρs ≤ -0.71 [CO, PG]). For T1ρ, correlations were considerably weaker and fewer (0.16 ≤ ρs ≤ -0.15). QMRI parameters are characterized in their biophysical properties and their sensitivity and specificity profiles in a basic scientific context. Although none of these is specific towards any particular cartilage constituent, T1 and T2⁎ reflect actual tissue compositional features more closely than T1ρ.