Magnetic resonance measurements of tissue quantity and quality using T2 * relaxometry predict temporal changes in the biomechanical properties of the healing ACL.

The purpose of this study was to develop a magnetic resonance T2 * relaxometry-based multiple linear regression model to predict the structural properties of the healing anterior cruciate ligament (ACL) over a 24-week healing period following ACL repair in Yucatan minipigs. Two hypotheses were tested: (i) that a regression model based on ACL sub-volumes containing short and long T2 * relaxation times would outperform a competing model based on sub-volumes of short T2 * relaxation times only; and (ii) that an optimized regression model would be capable of predicting ACL structural properties between 6 and 24 weeks post-repair. ACLs were imaged in 24 minipigs (8/group) at either 6, 12, or 24 weeks after ACL repair. The structural properties of the ACLs were determined from tensile failure tests. Four multiple linear regression models of increasing complexity were fitted to the data. Akaike Information Criterion values and Bland-Altman tests were used to compare model performance and to test the hypotheses. The structural properties predicted from the multiple linear regression model that was based on the change in ACL sub-volumes of both the short and long T2 * relaxation times over the healing period were in closest agreement to the measured values, suggesting that the amounts of both organized and disorganized collagen, and the change in these quantities over time, are required to predict the structural properties of healing ACLs accurately. CLINICAL SIGNIFICANCE: our time-specific, T2 *-based regression model may allow us to estimate the structural properties of ACL repairs in vivo longitudinally.