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Enhanced fracture risk assessment of spine using stochastically treated DXA image

使用随机处理的 DXA 图像增强脊柱骨折风险评估

基本信息

批准号：
8367230
负责人：
Xuanliang Neil Dong
金额：
$ 38.47万
依托单位：
UNIVERSITY OF TEXAS TYLER
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-01 至 2017-06-30
项目状态：
已结题

项目摘要

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.

描述（由申请人提供）：脊柱骨折是最常见的骨质疏松性骨折类型，50岁以上的女性中有三分之一，男性中有六分之一。众所周知，骨量的减少（以骨矿物质密度来量化）与骨折风险的增加有关。然而，骨矿物质密度本身并不能完全解释骨折风险的变化。除了骨量外，骨结构也被认为是导致骨折风险的另一个关键因素。尽管近年来取得了相当大的进展，表明三维成像，如micro-CT， pQCT和micro-MRI，可以提供与骨脆性相关的建筑信息，但这些技术在常规临床应用中仍然不切实际。因此，如果我们能够从双能x射线吸收仪（DXA）扫描的二维图像中骨矿物质密度的空间分布中找到与建筑信息相关的有用参数，那么利用简单的DXA扫描基于骨矿物质密度和分布的测量来评估骨脆弱性将是有希望的。我们的长期目标是从临床可行的措施中开发出高度准确预测脊柱骨折的技术。本应用程序的目的是确定如果使用一种新的随机图像处理方法增强，是否可以使用来自人体脊柱DXA扫描的2D图像的骨矿物质密度分布来提供骨骼易碎性的额外测量。本应用程序的中心假设是，从DXA扫描的2D图像量化的骨矿物质密度的空间分布与脊柱的建筑特性相关，通过将密度分布测量与DXA骨矿物质密度数据相结合，可以显著改善骨脆弱性的预测。我们的假设是在强有力的初步数据的基础上提出的，这些数据表明随机场理论可以用来量化骨矿物质密度的空间分布，并且随机模型中定义的参数与骨小梁的微结构和强度都有显著的相关性。两个具体的目标将被追求，以检验中心假设和实现这一应用程序的目标。在具体目标1中，我们将确定DXA脊柱图像中骨矿物质密度空间分布的随机参数与脊柱微结构的相关性。具体目标1的工作假设是，来自人体脊柱二维DXA图像的骨矿物质密度空间分布的仍然方差与来自骨小梁三维微ct图像的骨微结构有关。在具体目标2中，我们将确定增强的DXA方法在预测骨脆性方面的功效。假设从DXA扫描的2D脊柱图像中量化骨矿物质密度的空间分布，并结合骨矿物质密度，将比单独使用骨矿物质密度更好地预测骨强度。在完成这些研究后，我们预计将从DXA扫描的2D图像中建立一种经济有效的评估脊柱骨折风险的方法。我们期望这种方法可以改善骨折风险的预测和对治疗反应的监测。此外，该项目将为研究人员提供进行独立研究的机会，并为学生提供参与生物医学研究的经验，从而加强受资助机构的研究环境。