DESCRIPTION (provided by applicant): Spine fractures are the most common type of osteoporotic fractures, affecting one in three women and one in six men over the age of 50. It is well known that loss of bone mass, quantified by bone mineral density, is associated with the increasing risk of bone fractures. However, bone mineral density alone cannot fully explain changes in fracture risks. In addition to bone mass, bone architecture has been identified as another critical factor to fracture risk. Although considerable progress has been made in recent years, showing that 3D imaging, such as micro-CT, pQCT and micro-MRI, can provide the architectural information related to bone fragility, these techniques are still impractical in routne clinical applications. Thus, if we can find useful parameters, which are associated with architectural information, from the spatial distribution of bone mineral density in 2D images of Dual-energy X-ray absorptiometry (DXA) scans, it would be promising to utilize a simple DXA scan to assess bone fragility based on the measurements of both bone mineral density and distribution. Our long-term goal is to develop techniques for highly accurate prediction of spine fractures from clinically feasible measures. The objective of this application is to determine whether the distribution of bone mineral density from 2D images of DXA scans of human spines can be used to provide additional measures of bone fragility if enhanced using a novel stochastic image processing approach. The central hypothesis of this application is that the spatial distribution of bone mineral density quantified from 2D images of DXA scans is associated with the architectural properties of the spine, leading to significantly improved prediction of bone fragility by combining this measure of density distribution with DXA bone mineral density data. Our hypothesis has been formulated on the basis of strong preliminary data, which have shown that random field theory can be used to quantify the spatial distribution of bone mineral density and that the parameters defined in the stochastic model are significantly correlated with both microarchitecture and strength of trabecular bone. Two specific aims will be pursued to test the central hypothesis and accomplish the objective of this application. In specific aim 1, we will determine the correlation of the stochastic parameters of spatial distribution of bone mineral density of the DXA spine images with the microarchitecture of the spine. The working hypothesis for specific aim 1 is that the sill variance, a measure of spatial distribution of bone mineral density, from 2D DXA images of human spine is associated with bone micro-architecture from 3D micro-CT images of trabecular bone. In specific aim 2, we will determine the efficacy of the enhanced DXA approach in predicting bone fragility. It is postulated that quantification of spatial distribution of bone mineral density derived from 2D spine images of DXA scans, combined with bone mineral density, will predict bone strength better than using bone mineral density alone. At the completion of these studies, we anticipate that an economical and effective method for assessing the risk of spine fractures will be established from 2D images of DXA scans. We anticipate that this method could lead to improved prediction of fracture risk and monitoring of response to treatment. Additionally, this project will strengthen the research environment at the grantee institution by providing investigators opportunities to carry out independent research, and offering students experience and involvement in biomedical research. PUBLIC HEALTH RELEVANCE: Between 35% and 50% of all women over age 50 had at least one spine fracture. Therefore, it is critical to identify those at highest risk in the populaion and reduce the number of spine fractures. This project focuses on improving the accuracy of predicting fracture risk of spine using DXA densitometers by combining measures of bone mineral density and its distribution.
|Effective start/end date||7/1/12 → 6/30/16|
- National Institutes of Health: $384,728.00