Multi-Parametric Spatial Assessment of Bone with HR-pQCT

使用 HR-pQCT 对骨骼进行多参数空间评估

• 批准号: 9398825
• 负责人: Julio Carballido-Gamio
• 金额: $ 24.74万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9398825
• 关键词: Multi; Parametric; Spatial; Assessment; Bone

 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.

 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，使人骨折风险增加。在当今的美国，估计有 1000 万人已经患有这种疾病，估计还有近 3400 万人的骨密度较低，这使他们面临骨质疏松和骨折的风险增加。目前，骨折风险、衰老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，但研究表明BMD只能解释约70%-75%的强度方差，而其余方差则归因于其他因素如骨结构、拓扑、几何形状、组织成分、微损伤和生物力学因素的累积和协同效应。高分辨率外周定量计算机断层扫描 (HR-pQCT) 是一种无创体内成像技术，可描述许多特征，包括远端桡骨和远端胫骨的皮质骨和小梁骨的密度、几何形状、结构、拓扑和力学。迄今为止，HR-pQCT 图像已使用传统的定量方法进行分析，该方法对大范围感兴趣区域的骨骼特征进行平均。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）特征如何协同地促进骨强度。因此，传统方法无法捕获所研究效应的空间模式，而这对于理解潜在生物学至关重要。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的互补和相互依赖性质的 3D 技术进行分析。统计参数映射 (SPM) 是一种能够对受试者组之间的多参数映射进行 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着相关的区域，从而生成 3D 统计和 P 值图，以促进可视化，从而促进目标群体之间比较的解释。该提案的最终目标是建立一个框架，自动识别特定人群的相关骨子区域和特征，以便利用 HR-pQCT 有针对性地定量评估骨强度的空间分布和预测。为此，专门为 HR-pQCT 开发了 SPM 技术。为了评估 SPM 在临床科学中的潜力，我们建议将 SPM 应用于来自三项现有体内 HR-pQCT 研究的图像数据，这些研究调查：a）与性别和年龄相关的骨骼结构的区域变化； b) 由于前臂骨折造成的差异； c) 两种骨质疏松症治疗的纵向效应。