Decreased strength of trabecular bone is a direct effect of osteoporosis, which can be evaluated by finite element analysis. However, computational limitations have restricted previous trabecular bone analyses primarily to the linear domain. In addition, previous work was largely invasive and the corresponding finite element models were typically homogeneous.

Nonlinear heterogeneous finite element analysis was used to calculate trabecular bone apparent strength directly from in vivo micro computational tomography (micro-CT) scans. Through a series of validation experiments, it was shown that this nonlinear modeling is more accurate in evaluating trabecular bone mechanics in osteoporosis than previous work. A parameter driven set of material properties was employed in the finite element models using gray levels in the form of Hounsfield units as the independent variable. This enabled the finite element models to capture the variations of material properties of trabecular bone. The methods and techniques for converting the micro-CT scans into finite element models, defining the finite element models (element type, material properties characteristics) and solving the models are discussed in detail. In addition, the techniques developed for image preprocessing, such as image registration and image degradation, are also provided in this dissertation.

The scanning, image processing and modeling methods and techniques were applied to two groups of rabbits, an ovariectomy group and a control group, to evaluate the time-course of trabecular bone osteoporosis. Our experiment showed that ovariectomy significantly slows the normal bone strength increase over time observed in the control group. The strength increase over time was due to a combination of increased bone architecture indices such as volume fraction and trabecular thickness as well as increased material properties due to greater bone tissue density. Compared to heterogeneous models, the homogeneous models reflected less strength increase over time because they lack the capability to capture tissue level material property variations. Volume fraction analysis alone resulted in even lower predicted increases in bone strength because it could only monitor the bone apparent level density variation. Thus the nonlinear heterogeneous models, with parameter driven material definitions, are more accurate than other types of models or methods.