This work examines the use of UTE-T2* profile analysis to assess sub-surface knee cartilage structural changes in 26 ACL-reconstructed subjects and compares UTE-T2* metrics to kinetic and kinematic measures of walking. UTE-T2* profile characteristics detect differences between ACL-reconstructed and uninjured knees. Side-to-side differences in an individual’s UTE-T2* profile metrics correlate with side-to-side differences in their gait metrics. The results suggest that UTE-T2* profile characteristics of cartilage are a useful metric for identifying cartilage subsurface changes early after ACLR and may reflect matrix changes resulting from altered loading.
Twenty-six ACLR subjects (15F, 35±10yrs, 2.20±0.23yrs post-unilateral ACL-reconstruction) and 15 uninjured controls (6F, 31±9yrs) consented to participate in these IRB-approved studies. ACLR subjects underwent 3T MRI examination of both their ACLR and contralateral knees (MR 750, GE Healthcare, Milwaukee, WI). UTE-T2* maps were calculated via mono-exponential fitting of a series of T2*-weighted MR images acquired at eight TEs (32μs -16ms, non-uniform echo spacing) using a radial out 3-D cones acquisition7. Mean UTE-T2* values were calculated in 2 regions of interest (ROIs) weight-bearing deep cartilage (extending from the bone-cartilage interface through half of the cartilage thickness) of the central medial femoral condyle and tibial plateau (cMFC, cMTP). UTE-T2* profiles were computed for each subject by averaging UTE-T2* values across the width of the ROI, along each line of pixels parallel to the subchondral plate, at increasing distances from the bone-cartilage interface. Profiles were normalized to cartilage thickness using cubic interpolation. Slopes of the profile curves were estimated across the deepest 0-25% of tissue depth and also the middle 31-63% of tissue depth. UTE-T2* processing was performed with Matlab (TheMathWorks, MA).
All ACLR subjects underwent gait analysis, walking at normal self-selected speed. A 10-camera optoelectronic system (Qualisys, SE) and a force plate (Bertec, OH) were used to measure subjects’ motion at 120Hz. Knee kinematics and kinetics were calculated using BioMove software (Stanford University) and the point cluster technique8. Five knee kinetics metrics: adduction moments during loading response, late stance, and impulse over stance (KAM1, KAM2, KAM-IS), maximum extension and flexion moments (KEM, KFM); and two kinematic metrics: flexion angle at heel strike (KFA-HS) and average external rotation during stance (aExtRot); were compared to UTE-T2* means and profiles slopes using Pearson correlations (or Spearmans when data was non-normally distributed). Correlations results were adjusted for multiple comparisons using a Benjamini-Hochberg procedure with a false discovery rate of 0.2. ANOVA adjusted for multiple comparison assessed differences in mean UTE-T2* values across groups. Statistical analyses were performed with SPSS (IBM) and Excel (Microsoft).
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Table 1. Correlations between Gait and UTE-T2* metrics
*Indicates Spearman correlation. Side-to-side differences represent contralateral knee minus ACLR knee.Only significant correlations are included in the table.