MRI-derived articular cartilage strains predict patient-reported outcomes six months post anterior cruciate ligament reconstruction - Emily Y. Miller; Woowon Lee; Timothy Lowe; Hongtian Zhu; Pablo F. Argote; Danielle Dresdner; James Kelly; Rachel M. Frank; Eric McCarty; Jonathan Bravman; Daniel Stokes; Nancy C. Emery; et al. (Scientific Reports)
Abstract Anterior cruciate ligament (ACL) injuries lead to an increased risk of osteoarthritis (OA). However, efforts to diagnose OA before irreversible changes to the joint occur remain limited. In this work, we utilized both quantitative MRI (qMRI) and displacements under applied loading MRI (dualMRI) to determine if relaxometry measures derived from qMRI and strains derived from dualMRI correlate with patient-reported outcomes at six months post unilateral ACL reconstruction. Quantitative MRI (T2, T2*, T1ρ) measurements and dualMRI strains (transverse, axial, and shear strains) were quantified in the medial articular tibiofemoral cartilage of 35 participants at six-months post unilateral ACL reconstruction. The relationships between patient-reported outcome scores and all MRI metrics were quantified using general linear mixed-effects models and a combined best-fit multicontrast MRI model was then developed. Higher femoral shear and transverse strains were significantly correlated with worse patient-reported outcomes. No relaxometry measures were correlated with patient-reported outcome scores. We identified the best-fit model for predicting patient-reported outcome score using multiple MRI measures and patient-specific information. The best-fit model significantly predicted patient-reported outcome score (p < 0.001, R2 = 0.52) better than any one individual MRI metric alone. This work presents the first use of dualMRI in vivo in a cohort of participants at risk for developing osteoarthritis. Our results indicate that both shear and transverse strains are highly correlated with patient-reported outcome severity, and may represent early biomechanical changes associated with symptomatic burden, which could potentially inform future efforts to identify individuals at risk for developing osteoarthritis.