T2 * MR relaxometry and ligament volume are associated with the structural properties of the healing ACL

J Orthop Res. 2014 Apr;32(4):492-9. doi: 10.1002/jor.22563. Epub 2013 Dec 16.


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.

Keywords: ACL; MRI; biomechanics; ligament healing; structural properties.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Anterior Cruciate Ligament / physiopathology*
  • Anterior Cruciate Ligament Injuries
  • Anterior Cruciate Ligament Reconstruction
  • Knee Injuries / physiopathology*
  • Knee Injuries / surgery
  • Knee Joint / physiopathology*
  • Magnetic Resonance Imaging*
  • Models, Theoretical
  • Swine
  • Swine, Miniature