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

基本信息

批准号：
9274155
负责人：
Julio Carballido-Gamio
金额：
$ 26.95万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2021-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9274155
关键词：
Affect Age Aging Biology Biomechanics Bone Density Bone Diseases Bone structure Characteristics Clinical Clinical Sciences Data Development Diagnosis Dimensions Discrimination Disease Distal Elderly Etiology Exercise Forearm Fracture Fracture Gender Geometry Goals Hip region structure Hormonal Image Imagery Imaging Techniques Incidence Individual Information Distribution Machine Learning Maps Measures Mechanics Metabolic Methods Nature Organ Osteoporosis Patients Pattern Peripheral Persons Pharmaceutical Preparations Pharmacotherapy Population Postmenopause Process Property Public Health Radial Resolution Risk Roentgen Rays Role Skeleton Spatial Distribution Stimulus Structure Target Populations Techniques Testing Thick Time Tissues Treatment Efficacy Variant Vertebral column Woman Work X-Ray Computed Tomography age effect base bone bone quality bone strength cortical bone cost density experience fracture risk improved in vivo in vivo imaging innovation insight interest population based public health relevance response screening skeletal skeletal disorder spatial relationship substantia spongiosa tibia tool treatment response

项目摘要

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，导致骨折风险增加。在当今的美国，估计有 1000 万人患有这种疾病，估计还有近 3400 万人患有这种疾病。骨密度低，导致骨质疏松和骨折的风险增加。目前，骨折风险、衰老影响和治疗效果的确定主要基于面积测量的骨矿物质密度 (BMD)。 BMD 或基于体积 X 射线的成像技术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% 的强度变化，而其余的变化则归因于强度变化。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，可结合其他因素（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）的累积和协同效应。描述许多这些特征的成像技术，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 图像已使用平均骨特征的传统定量方法进行了分析。对大的感兴趣区域的平均骨骼特征（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骼强度。传统方法无法捕捉所研究效应的空间模式，而这是理解基础生物学的关键。骨骼是一种通过重塑不断适应的 3D 器官，因此应使用反映骨骼之间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，而是识别显着相关的区域。变量为该提案的最终目标是建立一个框架来自动识别相关的骨骼子区域和特征。在特定人群中使用 HR-pQCT 有针对性地评估骨强度的空间分布和预测为此，我们为 HR-pQCT 开发了专门的 SPM 技术，以评估 SPM 在临床科学中的潜力。 SPM从三个图像数据现有的体内 HR-pQCT 研究调查：a）与性别和年龄相关的骨结构的区域差异；b）前臂骨折引起的差异；c）两种骨质疏松症治疗的纵向影响。