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

• Published in: Journal of magnetic resonance imaging Vol. 50; no. 5; pp. 1561 - 1570
• Main Authors: Lange, Thomas, Taghizadeh, Elham, Knowles, Benjamin R., Südkamp, Norbert P., Zaitsev, Maxim, Meine, Hans, Izadpanah, Kaywan
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.11.2019; Wiley Subscription Services, Inc
• Subjects: Artificial neural networks; Cartilage; Compression; contact area; Femur; Field strength; Image processing; Image segmentation; Knee; Load; loading; Loads (forces); Magnetic resonance imaging; Mechanical loading; Neural networks; Optical tracking; patellofemoral cartilage; prospective motion correction; Rank tests; Regularization; Reproducibility; Statistical analysis; Statistical tests; Thickness; patellofemoral cartilage; magnetic resonance imaging; loading; thickness; contact area; prospective motion correction
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.26724

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.