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Development of a parametric finite element model of the proximal femur using statistical shape and density modelling

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  • Daniel Nicolella
  • Todd Bredbenner

Abstract

Skeletal fractures associated with bone mass loss are a major clinical problem and economic burden, and lead to significant morbidity and mortality in the ageing population. Clinical image-based measures of bone mass show only moderate correlative strength with bone strength. However, engineering models derived from clinical image data predict bone strength with significantly greater accuracy. Currently, image-based finite element (FE) models are time consuming to construct and are non-parametric. The goal of this study was to develop a parametric proximal femur FE model based on a statistical shape and density model (SSDM) derived from clinical image data. A small number of independent SSDM parameters described the shape and bone density distribution of a set of cadaver femurs and captured the variability affecting proximal femur FE strength predictions. Finally, a three-dimensional FE model of an ‘unknown’ femur was reconstructed from the SSDM with an average spatial error of 0.016 mm and an average bone density error of 0.037 g/cm3.

Suggested Citation

  • Daniel Nicolella & Todd Bredbenner, 2012. "Development of a parametric finite element model of the proximal femur using statistical shape and density modelling," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 15(2), pages 101-110.
    • Handle: RePEc:taf:gcmbxx:v:15:y:2012:i:2:p:101-110
    DOI: 10.1080/10255842.2010.515984
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