Our objective was to develop a non-invasive magnetic resonance (MR) method to predict the structural properties of a healing anterior cruciate ligament (ACL) using volume and T2 * relaxation time. We also compared our T2 *-based structural property prediction model to a previous model utilizing signal intensity, an acquisition-dependent variable. Surgical ACL transection followed by no treatment (i.e., natural healing) or bio-enhanced ACL repair was performed in a porcine model. After 52 weeks of healing, high-resolution MR images of the ACL tissue were collected. From these images, ligament volumes and T2 * maps were established. The structural properties of the ligaments were determined via tensile testing. Using the T2 * histogram profile, each ligament voxel was binned based on its T2 * value into four discrete tissue sub-volumes defined by specific T2 * intervals. The linear combination of the ligament sub-volumes binned by T2 * value significantly predicted maximum load, yield load, and linear stiffness (R(2) = 0.92, 0.82, 0.88; p < 0.001) and were similar to the previous signal intensity based method. In conclusion, the T2 * technique offers a highly predictive methodology that is a first step towards the development of a method that can be used to assess ligament healing across scanners, studies, and institutions.
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Biercevicz et al. (2013) studied this question.
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