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. T 1, T 1 ρ , and T 2⁎ 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 T 1 and T 2⁎ , in particular in the central region ( T 1: ρs ≥ 0.7 [FF, CFO], ρs ≤ -0.8 [CO, PG]; T 2⁎ : ρs ≥ 0.67 [FF, CFO], ρs ≤ -0.71 [CO, PG]). For T 1 ρ , 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, T 1 and T 2⁎ reflect actual tissue compositional features more closely than T 1 ρ .