Bone apparent and material densities examined by cone beam computed tomography and the Archimedes technique: comparison of the two methods and their results

Abstract

An understanding of bone apparent and material densities and how they vary within bone at the organ level is of great interest in the understanding of degenerative bone conditions and for biomedical engineering applications. The densities of bone tissue have been shown to appreciably influence the mechanical competency of bone tissue. In order to assess the density of bone in the body, it is important to ensure that the parameters being measured in vivo are truly representative of the real values that have been measured in vitro. To assess the densities of bone across the entire spectrum of available porosities, 112 samples from an elephant femur were assessed using the Archimedes method (water displacement) and by micro-computed tomography (μ-CT). Comparisons were drawn between the two methods to determine if the densities calculated by μ-CT were representative of physically measured densities. The results showed that the apparent densities measured over the entire spectrum were very similar but varied in the intermediate regions of bone tissue, probably due to an increased presence of osteoid, increased remodeling, or experimental error as these type of bone is known for the presence of regions of closed cell geometry in the cancellous architecture. It could be argued that the measurements taken by μ-CT are more reliable of bone density values for the mineralized regions of bone as the threshold is defined with respect to the absorption of X-rays by the mineral. In contrast, the Archimedes method thresholds everything with a density value above that of the surrounding medium, 1 (g cm−3) for water, and hence it is more sensitive to the presence of osteoid, soft collagenous matrix, and epithelial cell layers. Further research is required to optimize the parameters of scanning methods for the structural properties of different bone tissue porosities, which hopefully in turn will be able to provide a basis for the development of predictive remodeling models.