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

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.

Keywords:

• fracture risk
• bone strength
• finite element model
• statistical shape model
• parametric model

Acknowledgement

This research was supported by SwRI internal research project R9541 and NIH/NIAMS research grant AR052013.

Notes

1. Email: [email protected]

Additional information

Notes on contributors

Daniel P. Nicolella

1