No abstract

Insufficiency fractures occur when physiological loads are applied to bone deficient in mechanical resistance. A better understanding of pelvic mechanics and the effect of bone density alterations could lead to improved diagnosis and treatment of insufficiency fractures. This study aimed to develop and validate a subject-specific three-dimensional (3D) finite element (FE) model of a pelvis, to analyse pelvic strains as a function of interior and cortical surface bone density, and to compare high strain regions with common insufficiency fracture sites. The FE model yielded strong agreement between experimental and model strains. By means of the response surface method, changes to cortical surface bone density using the FE model were found to have a 60 per cent greater influence compared with changes in interior bone density. A small interaction was also found to exist between surface and interior bone densities (< 3 per cent), and a non-linear effect of surface bone density on strain was observed. Areas with greater increases in average principal strains with reductions in density in the FE model corresponded to areas prone to insufficiency fracture. Owing to the influence of cortical surface bone density on strain, it may be considered a strong global (non-linear) indicator for insufficiency fracture risk.

Quantitative assessment of metastatic disease in bone is often considered immeasurable and, as such, patients with skeletal metastases are often excluded from clinical trials. In order to effectively quantify the impact of metastatic tumor involvement in the spine, accurate segmentation of the vertebra is required. Manual segmentation can be accurate but involves extensive and time-consuming user interaction. Potential solutions to automating segmentation of metastatically involved vertebrae are demons deformable image registration and level set methods. The purpose of this study was to develop a semiautomated method to accurately segment tumor-bearing vertebrae using the aforementioned techniques. By maintaining morphology of an atlas, the demons-level set composite algorithm was able to accurately differentiate between trans-cortical tumors and surrounding soft tissue of identical intensity. The algorithm successfully segmented both the vertebral body and trabecular centrum of tumor-involved and healthy vertebrae. This work validates our approach as equivalent in accuracy to an experienced user.