An Adaptive Computational Model for Predicting the Density Distribution of the Proximal Femur

Abstract

A custom algorithm was developed to simulate adaptive bone
remodeling. The process of adaptive bone remodeling can be
simulated with a self-optimizing finite element method (FEM). The
basic remodeling rule attempts to obtain a constant value for the
strain energy per unit bone mass, by adapting density. The precise
solution is dependent on the loads, initial conditions and the
parameters in the remodeling rule. The aim of this study was to
identify how the bone density distribution of the proximal femur
was affected by parameters which govern the remodeling process.
The forces at different phases of the gait cycle were applied as
boundary conditions. The bone density distributions from these
forces were averaged to estimate the density distribution in the
proximal femur. The effect of varying the spatial influence
function, and the influence range on the converged solution were
investigated. It was shown that varying these parameters within
reasonable upper and lower bounds had very little impact on the
qualitative form of the converged solution. In all cases, the
solutions obtained are comparable with the actual density in the
proximal femur, as measured by DEXA scans.