Quantification of patellofemoral cartilage deformation and contact area changes in response to static loading via high-resolution MRI with prospective motion correction
| Autor: | Elham Taghizadeh, Kaywan Izadpanah, Thomas Lange, Norbert P. Südkamp, Hans Meine, Benjamin R. Knowles, Maxim Zaitsev |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Materials science Wilcoxon signed-rank test Standard deviation 030218 nuclear medicine & medical imaging 03 medical and health sciences Motion Patellofemoral Joint 0302 clinical medicine Imaging Three-Dimensional medicine Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Femur Knee Reproducibility medicine.diagnostic_test Cartilage Reproducibility of Results Magnetic resonance imaging Magnetic Resonance Imaging Healthy Volunteers medicine.anatomical_structure Prospective motion correction Contact area Biomedical engineering |
| Zdroj: | Journal of magnetic resonance imaging : JMRIReferences. 50(5) |
| ISSN: | 1522-2586 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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