Quantification of patellofemoral cartilage deformation and contact area changes in response to static loading via high‐resolution MRI with prospective motion correction

BACKGROUND: Higher-resolution MRI of the patellofemoral cartilage under loading is hampered by subject motion since knee flexion is required during the scan. PURPOSE: To demonstrate robust quantification of cartilage compression and contact area changes in response to in situ loading by means of MRI with prospective motion correction and regularized image postprocessing. STUDY TYPE: Cohort study. SUBJECTS: Fifteen healthy male subjects. FIELD STRENGTH: 3 T. SEQUENCE: Spoiled 3D gradient-echo sequence augmented with prospective motion correction based on optical tracking. Measurements were performed with three different loads (0/200/400 N). ASSESSMENT: Bone and cartilage segmentation was performed manually and regularized with a deep-learning approach. Average patellar and femoral cartilage thickness and contact area were calculated for the three loading situations. Reproducibility was assessed via repeated measurements in one subject. STATISTICAL TESTS: Comparison of the three loading situations was performed by Wilcoxon signed-rank tests. RESULTS: Regularization using a deep convolutional neural network reduced the variance of the quantified relative load-induced changes of cartilage thickness and contact area compared to purely manual segmentation (average reduction of standard deviation by ∼50%) and repeated measurements performed on the same subject demonstrated high reproducibility of the method. For the three loading situations (0/200/400 N), the patellofemoral cartilage contact area as well as the mean patellar and femoral cartilage thickness were significantly different from each other (P < 0.05). While the patellofemoral cartilage contact area increased under loading (by 14.5/19.0% for loads of 200/400 N), patellar and femoral cartilage thickness exhibited a load-dependent thickness decrease (patella: -4.4/-7.4%, femur: -3.4/-7.1% for loads of 200/400 N). DATA CONCLUSION: MRI with prospective motion correction enables quantitative evaluation of patellofemoral cartilage deformation and contact area changes in response to in situ loading. Regularizing the manual segmentations using a neural network enables robust quantification of the load-induced changes. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1561-1570.