Finite element analysis for prediction of bone strength View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-08-07

AUTHORS

Philippe K Zysset, Enrico Dall'Ara, Peter Varga, Dieter H Pahr

ABSTRACT

Finite element (FE) analysis has been applied for the past 40 years to simulate the mechanical behavior of bone. Although several validation studies have been performed on specific anatomical sites and load cases, this study aims to review the predictability of human bone strength at the three major osteoporotic fracture sites quantified in recently completed in vitro studies at our former institute. Specifically, the performance of FE analysis based on clinical computer tomography (QCT) is compared with the ones of the current densitometric standards, bone mineral content, bone mineral density (BMD) and areal BMD (aBMD). Clinical fractures were produced in monotonic axial compression of the distal radii, vertebral sections and in side loading of the proximal femora. QCT-based FE models of the three bones were developed to simulate as closely as possible the boundary conditions of each experiment. For all sites, the FE methodology exhibited the lowest errors and the highest correlations in predicting the experimental bone strength. Likely due to the improved CT image resolution, the quality of the FE prediction in the peripheral skeleton using high-resolution peripheral CT was superior to that in the axial skeleton with whole-body QCT. Because of its projective and scalar nature, the performance of aBMD in predicting bone strength depended on loading mode and was significantly inferior to FE in axial compression of radial or vertebral sections but not significantly inferior to FE in side loading of the femur. Considering the cumulated evidence from the published validation studies, it is concluded that FE models provide the most reliable surrogates of bone strength at any of the three fracture sites. More... »

PAGES

386

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/bonekey.2013.120

    DOI

    http://dx.doi.org/10.1038/bonekey.2013.120

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1006256068

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24422106


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