Multi-Parametric Spatial Assessment of Bone with HR-pQCT

 描述（由申请人提供）：骨质疏松症是一种骨骼疾病，其特征是骨强度受损，易使人骨折风险增加。在今天的美国，估计有1000万人已经患有这种疾病，估计还有近3400万人骨密度低，使他们患骨质疏松症和骨折的风险增加。目前，骨折风险、老化效应和治疗效果的确定主要基于通过基于面积或体积X射线的成像技术测量的骨矿物质密度（BMD）。BMD可以在一定程度上预测骨强度和骨折风险，然而，研究表明，BMD仅解释约70%-75%的强度方差，而其余的方差归因于其他因素的累积和协同作用，如骨结构，拓扑结构，几何形状，组织成分，微损伤和生物力学因素。高分辨率外周定量计算机断层扫描（HR-pQCT）是一种非侵入性体内成像技术，可描述许多这些特征，包括桡骨远端和胫骨远端皮质骨和松质骨的密度、几何形状、结构、拓扑结构和力学。迄今为止，HR-pQCT图像已使用传统的定量方法进行了分析，该方法对大面积感兴趣区域的骨骼特征进行了平均。平均骨特征（单参数）或其统计组合（多参数）的单独量化忽略了这些三维（3D）特征如何协同促进骨强度。因此，传统的方法无法捕捉正在研究的效应的空间模式，这是理解潜在生物学的关键。骨骼是一个通过重塑不断适应的3D器官，因此应使用反映不同骨骼特征互补和相互依赖性质的3D技术进行分析。统计参数映射（SPM）是一种能够在受试者组之间对多参数图进行3D空间比较的技术。这种数据驱动的过程不是测量任意或主观感兴趣体积的汇总属性，而是通过有效的统计测试识别与感兴趣变量显著相关的区域，从而生成3D统计和P值图，便于可视化，从而解释目标人群之间的比较。该提案的最终目标是建立一个框架，以自动识别特定人群中的相关骨亚区和特征，从而使用HR-pQCT对骨强度的空间分布和预测进行有针对性的定量评估。为此，针对HR-pQCT开发了专门的SPM技术。为了评估SPM在临床科学中的潜力，我们建议将SPM应用于三项现有体内HR-pQCT研究的图像数据，研究：a）与性别和年龄相关的骨结构区域变化; B）前臂骨折引起的差异;以及c）两种骨质疏松症治疗的纵向效应。

 DESCRIPTION (provided by applicant): Osteoporosis is a skeletal disorder characterized by compromised bone strength predisposing a person to an increased risk of fracture. In the U.S. today, 10 million individuals are estimated to already have the disease and almost 34 million more are estimated to have low bone density, placing them at increased risk for osteoporosis and broken bones. Currently, determination of fracture risk, aging effects, and therapeutic efficacy is primarily based on bone mineral density (BMD) measured by areal or volumetric X-ray-based imaging techniques. BMD can predict bone strength and fracture risk to some extent, however, studies have shown that BMD only explains about 70%-75% of the variance in strength, while the remaining variance has been attributed to the cumulative and synergistic effect of other factors such as bone structure, topology, geometry, tissue composition, microdamage, and biomechanical factors. High-resolution peripheral quantitative computed tomography (HR-pQCT) is a noninvasive in-vivo imaging technique which depicts many of these features, including density, geometry, structure, topology, and mechanics of cortical and trabecular bone in the distal radius and distal tibia. To date HR-pQCT imagery has been analyzed using conventional quantitative approaches that average bone features over large regions of interest. The individual quantification of average bone features (uni-parametric) or their statistical combination (multi-parametric) disregard how these three-dimensional (3D) features synergistically contribute to bone strength. As a result the traditional methods fail to capture the spatial patterning of the effect being studied, which is key to understanding the underlying biology. Bone is a 3D organ experiencing constant adaptation through remodeling, and should therefore be analyzed with 3D techniques that reflect the complementary and interdependent nature of different bone features. Statistical parametric mapping (SPM) is a technique that enables 3D spatial comparisons of multi-parametric maps between groups of subjects. Instead of measuring summary properties for arbitrary or subjective volumes of interest, this data-driven process identifies regions significantly associated with a variable of interest through valid statistical tests, thus generating 3D statistical and P-value maps that facilitate the visualization and consequently the interpretation of comparisons between target populations. The ultimate goal of this proposal is to establish a framework to automatically identify relevant bone sub-regions and features in specific populations for the targeted quantitative assessment of the spatial distribution and prediction of bone strength using HR-pQCT. For this purpose, specialized SPM techniques have been developed for HR-pQCT. To evaluate the potential of SPM in clinical science, we propose to apply SPM to image data from three existing in-vivo HR-pQCT studies investigating: a) regional variations in bone structure related to gender and age; b) differences due to fracture of the forearm; and c) longitudinal effects of two osteoporosis treatments.