PROBABILISTIC SHAPE-BASED FINITE ELEMENT MODELING OF BABOON FEMURS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Osteoporosis, characterized by bone fragility and increased susceptibility to fracture, is an age-related health problem of immediate public health concern. The well being of 43.6 million people in the U.S. alone is currently threatened by osteoporosis or low bone mass. Individual suffering and public health costs resulting from the projected increase in osteoporotic fracture incidence as the U.S. population ages are motivating intensive research into the factors contributing to this painful and debilitating condition. Of particular concern is development of new techniques for predicting risk of bone fracture. Osteoporosis is a complex disease resulting from a number of genetic and environmental factors affecting bone mass and architecture, and, in turn, bone¿s resistance to fracture. Studies show that a great deal of osteoporosis fracture risk is independent of bone mass. This has prompted researchers to expand their investigations of osteoporosis fracture risk to other measures of bone quality, including evaluations of bone shape and the contribution of shape differences to the probability of fracture. Probabilistic shape-based finite element modeling of baboons femurs The specific aims of this study are to investigate the effectiveness and feasibility of developing probabilistic shape-based finite element models that may be useful for predicting fracture risk for individuals. Such models will be evaluated based on the amount of variation captured by the model. Effective modeling should reduce the number of random variables, thereby reducing the computational burden when performing probabilistic analysis.