Abstract
OBJECTIVE: We sought to measure the deformation of tibiofemoral cartilage immediately following a 3-mile treadmill run, as well as the recovery of cartilage thickness the following day. To enable these measurements, we developed and validated deep learning models to automate tibiofemoral cartilage and bone segmentation from double-echo steady-state magnetic resonance imaging (MRI) scans. DESIGN: Eight asymptomatic male participants arrived at 7 a.m., rested supine for 45 min, underwent pre-exercise MRI, ran 3 miles on a treadmill, and finally underwent post-exercise MRI. To assess whether cartilage recovered to its baseline thickness, participants returned the following morning at 7 a.m., rested supine for 45 min, and underwent a final MRI session. These images were used to generate 3D models of the tibia, femur, and cartilage surfaces at each time point. Site-specific tibial and femoral cartilage thicknesses were measured from each 3D model. To aid in these measurements, deep learning segmentation models were developed. RESULTS: All trained deep learning models demonstrated repeatability within 0.03 mm or approximately 1 % of cartilage thickness. The 3-mile run induced mean compressive strains of 5.4 % (95 % CI = 4.1 to 6.7) and 2.3 % (95 % CI = 0.6 to 4.0) for the tibial and femoral cartilage, respectively. Furthermore, both tibial and femoral cartilage thicknesses returned to within 1 % of baseline thickness the following day. CONCLUSIONS: The 3-mile treadmill run induced a significant decrease in both tibial and femoral cartilage thickness; however, this was largely ameliorated the following morning.