Quantification of patellofemoral cartilage deformation and contact area changes in response to static loading via...

Higher-resolution MRI of the patellofemoral cartilage under loading is hampered by subject motion since knee flexion is required during the scan.

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.

Cohort study.

Fifteen healthy male subjects.

3 T.

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).

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.

Comparison of the three loading situations was performed by Wilcoxon signed-rank tests.

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).

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.

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1561-1570.