Purpose: Medical imaging has the potential to noninvasively diagnose early disease onset and monitor the success of repair therapies. Unfortunately, few reliable imaging biomarkers exist to detect cartilage diseases before advanced degeneration in the tissue.
Method: In this study, we quantified the ability to detect osteoarthritis (OA) severity in human cartilage explants using a multicontrast magnetic resonance imaging (MRI) approach, inclusive of novel displacements under applied loading by MRI, relaxivity measures, and standard MRI.
Results: Displacements under applied loading by MRI measures, which characterized the spatial micromechanical environment by 2D finite and Von Mises strains, were strong predictors of histologically assessed OA severity, both before and after controlling for factors, e.g., patient, joint region, and morphology. Relaxivity measures, sensitive to local macromolecular weight and composition, including T1ρ, but not T1 or T2, were predictors of OA severity. A combined multicontrast approach that exploited spatial variations in tissue biomechanics and extracellular matrix structure yielded the strongest relationships to OA severity.
Conclusion: Our results indicate that combining multiple MRI-based biomarkers has high potential for the noninvasive measurement of OA severity and the evaluation of potential therapeutic agents used in the treatment of early OA in animal and human trials.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3732803 | PMC |
| http://dx.doi.org/10.1002/mrm.24725 | DOI Listing |
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