Abstract
Cartilage degeneration is associated with tissue softening and represents the hallmark change of osteoarthritis. Advanced quantitative Magnetic Resonance Imaging (qMRI) techniques allow the assessment of subtle tissue changes not only of structure and morphology but also of composition. Yet, the relation between qMRI parameters on the one hand and microstructure, composition and the resulting functional tissue properties on the other hand remain to be defined. To this end, a Finite-Element framework was developed based on an anisotropic constitutive model of cartilage informed by sample-specific multiparametric qMRI maps, obtained for eight osteochondral samples on a clinical 3.0 T MRI scanner. For reference, the same samples were subjected to confined compression tests to evaluate stiffness and compressibility. Moreover, the Mankin score as an indicator of histological tissue degeneration was determined. The constitutive model was optimized against the resulting stress responses and informed solely by the sample-specific qMRI parameter maps. Thereby, the biomechanical properties of individual samples could be captured with good-to-excellent accuracy (mean R2 [square of Pearson’s correlation coefficient]: 0.966, range [min, max]: 0.904, 0.993; mean Ω [relative approximated error]: 33%, range [min, max]: 20%, 47%). Thus, advanced qMRI techniques may be complemented by the developed computational model of cartilage to comprehensively evaluate the functional dimension of non-invasively obtained imaging biomarkers. Thereby, cartilage degeneration can be perspectively evaluated in the context of imaging and biomechanics.
Highlights
The working hypotheses of the study were that (1) the complex relation between the stress response of the tissue and its quantitative Magnetic Resonance Imaging (qMRI) appearance in terms of respective T1, T1ρ, T2 and T2* maps may be translated into a refined constitutive model of cartilage tissue and (2) the functional properties of the tissue can be described by this model following its comprehensive optimization
The most important finding of this study is that sample-specific and spatially resolved qMRI data may be integrated into a computational model of cartilage to (1) emulate structural and compositional tissue parameters and to (2) reliably capture cartilage functional properties
QMRI data were used as input variables in a sample-specific manner, while the remaining parameters were kept constant in efforts to keep the model complexity manageable
Summary
Functional MRI techniques (synonymous with quantitative MRI [qMRI]) such as T2, T2* and T1ρ mapping have been developed and validated in a variety of scientific and clinical contexts to characterize extracellular matrix properties of cartilage[6,7,8] and provide measures related to tissue composition and structure[4,9]. Our group studied correlations between measured qMRI parameter maps and modelled volume fractions to better refine each qMRI parameter’s sensitivity and specificity profile[15]. The working hypotheses of the study were that (1) the complex relation between the stress response of the tissue and its qMRI appearance in terms of respective T1, T1ρ, T2 and T2* maps may be translated into a refined constitutive model of cartilage tissue and (2) the functional properties of the tissue can be described by this model following its comprehensive optimization
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