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Computed tomography-based finite element analysis predicts failure loads and fracture patterns for vertebral sections.

Authors :
Silva MJ
Keaveny TM
Hayes WC
Source :
Journal of orthopaedic research : official publication of the Orthopaedic Research Society [J Orthop Res] 1998 May; Vol. 16 (3), pp. 300-8.
Publication Year :
1998

Abstract

Computed tomography-based finite element analysis represents a powerful research tool for investigating the mechanics of skeletal fractures. To provide evidence that this technique can be used to predict failure loads and fracture patterns for bone structures, we compared the observed and predicted failure behaviors of 18 midsagittal sections, 10 mm thick, cut from human vertebral bodies. The specimens were scanned by computed tomography, and finite element models were generated with use of empirically determined density-property relations to assign element-specific material properties. The specimens were loaded to failure in uniaxial compression, and the models were analyzed under matching conditions. The models provided predictions of yield load that were strongly correlated with experimentally measured values (r2 > 0.86) and were typically within 25% of measured values. Predicted stiffness values were moderately correlated with measured values, but large absolute differences existed between them. Comparisons between regions of observed fracture and of high predicted strain indicated that strain was an accurate indicator of the pattern of local fracture in more than two-thirds of the bone specimens. In addition, strain contour plots provided better indicators of local fracture than did stress plots in these heterogeneous bone structures. We conclude that computed tomography-based finite element analysis can be used successfully to predict both global and local failure behavior of simplified skeletal structures.

Details

Language :
English
ISSN :
0736-0266
Volume :
16
Issue :
3
Database :
MEDLINE
Journal :
Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Publication Type :
Academic Journal
Accession number :
9671924
Full Text :
https://doi.org/10.1002/jor.1100160305