Hans Meine
Head of Image Analysis and Deep Learning
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About
Hans Meine is the Head of Image Analysis and Deep Learning at the Fraunhofer-Institut für Digitale Medizin MEVIS, Germany. His research focuses on advancements in image analysis, deep learning techniques in medical imaging, and the development of AI applications for clinical practices. Recent work includes automated tooth labeling in panoramic X-rays, predictive modeling for ACL reconstruction, and privacy-preserving data analysis approaches in healthcare.
Articles (11)
Year:
2025
Year:
2025
Year:
2024
Background: Patellofemoral instability (PFI) leads to chronic knee pain, reduced performance and chondromalacia patellae with consecutive osteoarthritis. Therefore, determining the exact patellofemoral contact mechanism, as well as the factors leading to PFI, is of great importance. The present study compares in vivo patellofemoral kinematic parameters and the contact mechanism of volunteers with healthy knees and patients with low flexion patellofemoral instability (PFI). The study was performed with a high-resolution dynamic MRI. Material/Methods: In a prospective cohort study, the patellar shift, patella rotation and the patellofemoral cartilage contact areas (CCA) of 17 patients with low flexion PFI were analyzed and compared with 17 healthy volunteers, matched via the TEA distance and sex, in unloaded and loaded conditions. MRI scans were carried out for 0°, 15° and 30° knee flexion in a custom-designed knee loading device. To suppress motion artifacts, motion correction was performed using a moiré phase tracking system with a tracking marker attached to the patella. The patellofemoral kinematic parameters and the CCA was calculated on the basis of semi-automated cartilage and bone segmentation and registrations. Results: Patients with low flexion PFI showed a significant reduction in patellofemoral CCA for 0° (unloaded: p = 0.002, loaded: p = 0.004), 15° (unloaded: p = 0.014, loaded: p = 0.001) and 30° (unloaded: p = 0.008; loaded: p = 0.001) flexion compared to healthy subjects. Additionally, patients with PFI revealed a significantly increased patellar shift when compared to volunteers with healthy knees at 0° (unloaded: p = 0.033; loaded: p = 0.031), 15° (unloaded: p = 0.025; loaded: p = 0.014) and 30° flexion (unloaded: p = 0.030; loaded: p = 0.034) There were no significant differences for patella rotation between patients with PFI and the volunteers, except when, under load at 0° flexion, PFI patients showed increased patellar rotation (p = 0.005. The influence of quadriceps activation on the patellofemoral CCA is reduced in patients with low flexion PFI. Conclusion: Patients with PFI showed different patellofemoral kinematics at low flexion angles in both unloaded and loaded conditions compared to volunteers with healthy knees. Increased patellar shifts and decreased patellofemoral CCAs were observed in low flexion angles. The influence of the quadriceps muscle is diminished in patients with low flexion PFI. Therefore, the goal of patellofemoral stabilizing therapy should be to restore a physiologic contact mechanism and improve patellofemoral congruity for low flexion angles.
Year:
2023
Year:
2023
Year:
2023
Year:
2022
Year:
2022
Year:
2022
Year:
2022
Year:
2021
Collaborators (10)
Felix Thielke
Fraunhofer Institute for Digital Medicine
Ulrich Betz
-
Jürgen Konradi
University Medical Center of the Johannes Gutenberg University Mainz
Toralf Kirsten
Full Professor of Medical Data Science
Leipzig University
Hagen Schmal
University of Southern Denmark
Marc Christian Metzger
University Hospital
Kaywan Izadpanah
-
Horst Hahn
Professor of Digital Medicine
University of Bremen
Andrea Schenk
Professor for Computer-Assisted Diagnosis and Therapy
Hannover Medical School
Elham Taghizadeh
Fraunhofer Institute for Digital Medicine
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