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Journal of biomechanics · Jan 2019
Virtual structural analysis of tibial fracture healing from low-dose clinical CT scans.
- Peter Schwarzenberg, Michael M Maher, James A Harty, and Hannah L Dailey.
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, USA. Electronic address: Peter@lehigh.edu.
- J Biomech. 2019 Jan 23; 83: 49-56.
AbstractQuantitative assessment of bone fracture healing remains a significant challenge in orthopaedic trauma research. Accordingly, we developed a new technique for assessing bone healing using virtual mechano-structural analysis of computed tomography (CT) scans. CT scans from 19 fractured human tibiae at 12 weeks after surgery were segmented and prepared for finite element analysis (FEA). Boundary conditions were applied to the models to simulate a torsion test that is commonly used to access the structural integrity of long bones in animal models of fracture healing. The output of each model was the virtual torsional rigidity (VTR) of the healing zone, normalized to the torsional rigidity of each patient's virtually reconstructed tibia. This provided a structural measure to track the percentage of healing each patient had undergone. Callus morphometric measurements were also collected from the CT scans. Results showed that at 12 weeks post-op, more than 75% of patients achieved a normalized VTR (torsional rigidity relative to uninjured bone) of 85% or above. The predicted intact torsional rigidities compared well with published cadaveric data. Across all patients, callus volume and density were weakly and non-significantly correlated with normalized VTR and time to clinical union. Conversely, normalized VTR was significantly correlated with time to union (R2 = 0.383, p = 0.005). This suggests that fracture scoring methods based on the visual appearance of callus may not accurately predict mechanical integrity. The image-based structural analysis presented here may be a useful technique for assessment of bone healing in orthopaedic trauma research.Copyright © 2018 Elsevier Ltd. All rights reserved.
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