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

Area of Science:

• Biophysics
• Biomaterials Science
• Medical Imaging

Background:

• Quantitative magnetic resonance imaging (qMRI) shows promise for early cartilage degeneration detection.
• Current understanding lacks consensus on which cartilage components influence qMRI signal properties.

Purpose of the Study:

• To investigate the relationship between specific cartilage components and qMRI signal properties.
• To determine the sensitivity and specificity of T1, T1ρ, and T2* parameters in reflecting cartilage composition.

Main Methods:

• Generated T1, T1ρ, and T2* maps from cartilage samples (n=8) using a 3.0-T MRI system.
• Employed a numerical model to quantify fluid fraction (FF), proteoglycan (PG), collagen (CO) content, and collagen fiber orientation (CFO).
• Correlated qMRI parameters with modelled tissue parameters pixel-wise and region-specifically using Spearman's correlation coefficient (ρs).

Main Results:

• Significant correlations were observed between T1 and T2* parameters and cartilage compositional parameters (FF, CFO, CO, PG), particularly in the central region.
• T1 showed strong correlations with FF (ρs ≥ 0.7), CFO (ρs ≥ 0.7), CO (ρs ≤ -0.8), and PG (ρs ≤ -0.8).
• T2* demonstrated strong correlations with FF (ρs ≥ 0.67), CFO (ρs ≥ 0.67), CO (ρs ≤ -0.71), and PG (ρs ≤ -0.71).
• T1ρ exhibited considerably weaker and fewer correlations (0.16 ≤ ρs ≤ -0.15).

Conclusions:

• T1 and T2* qMRI parameters reflect cartilage compositional features more closely than T1ρ.
• While no single qMRI parameter is specific to one cartilage constituent, T1 and T2* offer valuable insights into tissue composition for early degeneration detection.