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. 1, 1, and 2 maps of cartilage samples ( = 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 . Significant correlations were found between modelled compositional parameters and 1 and 2, in particular in the central region (1: ≥ 0.7 [FF, CFO], ≤ -0.8 [CO, PG]; 2: ≥ 0.67 [FF, CFO], ≤ -0.71 [CO, PG]). For 1, correlations were considerably weaker and fewer (0.16 ≤ ≤ -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, 1 and 2 reflect actual tissue compositional features more closely than 1.