Quantitative MRI Biomarkers to Predict Risk of Reinjury Within 2 Years After Bridge-Enhanced ACL Restoration.

Background: Quantitative magnetic resonance imaging (qMRI) methods were developed to establish the integrity of healing anterior cruciate ligaments (ACLs) and grafts. Whether qMRI variables predict risk of reinjury is unknown.

Purpose: To determine if qMRI measures at 6 to 9 months after bridge-enhanced ACL restoration (BEAR) can predict the risk of revision surgery within 2 years of the index procedure.

Preoperative Risk Factors for Subsequent Ipsilateral ACL Revision Surgery After an ACL Restoration Procedure.

Background: Anterior cruciate ligament (ACL) revision surgery is challenging for both patients and surgeons. Understanding the risk factors for failure after bridge-enhanced ACL restoration (BEAR) may help with patient selection for ACL restoration versus ACL reconstruction.

Purpose: To identify the preoperative risk factors for ACL revision surgery within the first 2 years after BEAR.

Effects of Initial Graft Tension and Patient Sex on Knee Osteoarthritis Outcomes After ACL Reconstruction: A Randomized Controlled Clinical Trial With 10- to 12-Year Follow-up.

Background: The initial graft tension applied during anterior cruciate ligament (ACL) graft fixation may promote posttraumatic osteoarthritis (PTOA).

Purpose/hypothesis: This study sought to assess the effect of initial graft tension and patient sex on PTOA outcomes at 10 to 12 years after ACL reconstruction (ACLR). The hypothesis was that there would be no group- or sex-based differences in outcomes.

Reproducibility and postacquisition correction methods for quantitative magnetic resonance imaging of the anterior cruciate ligament (ACL).

Quantitative magnetic resonance imaging has been used to evaluate the structural integrity of knee joint structures. However, variations in acquisition parameters between scanners pose significant challenges. Understanding the effect of small differences in acquisition parameters for quantitative sequences is vital to the validity of cross-institutional studies, and for the harmonization of large, heterogeneous datasets to train machine learning models.