Localizing longitudinal changes in volumetric bone mineral density after acute knee injury using voxel-based morphometry
Abstract
Osteoarthritis (OA) is a progressive, degenerative whole joint disease that leads to pain, stiffness, and substantial reductions in physical activity and quality of life. Since the aetiology of OA is poorly understood, studying at-risk individuals, such as patients who have sustained an anterior cruciate ligament (ACL) injury, provides a unique opportunity to investigate early disease mechanisms. Early joint changes after an ACL injury often manifest in the peri-articular bone, which can be accurately captured using X-ray-based imaging modalities (e.g., dual energy quantitative computed tomography; DE-QCT) that capture bone mineral density (BMD). Voxelbased morphometry (VBM) can provide a comprehensive assessment of the entire bone structure through voxel-by-voxel comparisons, revealing both spatial and temporal patterns in BMD changes. The aim of this thesis was to develop and implement a voxel-wise analysis pipeline for peri-articular bone, enabling the detailed assessment of peri-articular BMD changes over the three years following ACL injury. Population-based atlases of the distal femur, proximal tibia, and patella were developed to facilitate voxel-wise comparisons. The precision of BMD measurements was evaluated both globally and locally, demonstrating excellent global reproducibility with localized variability at the voxel level. The established pipeline was applied to study the spatiotemporal BMD changes in the ACL-injured and contralateral knees, revealing region specific and time-dependent adaptations. A key finding was that early BMD losses often overlapped regions of bbone marrow lesions. This work highlights the value of combining DE-QCT and VBM for understanding of bone adaptations following ACL injury and its potential role in post-traumatic OA development.
Fields of Study & Subjects
Bone health and osteoporosis research
Knee injuries and reconstruction techniques
Medical Imaging and Analysis
Publication details
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Thesis