An integrative statistics-guided image-based multi-scale lung model

综合统计引导的基于图像的多尺度肺模型

• 批准号: 8714034
• 负责人: CHING-LONG LIN
• 金额: $ 61.82万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8714034
• 关键词: Air; Airway Resistance; Algorithms; Animals; Asthma; Back; Bacteria; Biological Markers; Breathing; Caliber; Chronic Obstructive Airway Disease; Classification; Cluster Analysis; Data; Data Set; Databases; Deposition; Environmental air flow; Epidemiologic Studies; Exhibits; Funding; Gender; Genetic; Goals; Hot Spot; Image; Individual; Inflammation; Iowa; Irritants; Label; Length; Letters; Leukocytes; Liquid substance; Lobe; Location; Longitudinal Studies; Lung; Measurement; Modeling; Outcome Measure; Particulate; Patients; Performance; Phenotype; Photons; Population; Population Analysis; Process; Pulmonary Emphysema; Pulmonary function tests; Radiology Specialty; Research; Resistance; Respiratory physiology; Rotation; Smoking History; Statistical Methods; Statistical Models; Stress; Structure-Activity Relationship; Techniques; Testing; Thick; Tissues; Toxin; Trees; United States National Institutes of Health; University Hospitals; X-Ray Computed Tomography; airway inflammation; asthmatic airway; base; computer cluster; computer framework; data modeling; density; human data; human subject; image registration; improved; lung imaging; lung volume; normal aging; particle; programs; public health relevance; research study; simulation; single photon emission computed tomography; statistics; tool; validation studies

DESCRIPTION (provided by applicant): The ultimate goal of the research is to build a new computational framework for assessment and prediction of lung function through integration of statistical analysis of population data with prediction of function in individual subjects via a muti-scale computational fluid dynamics (CFD) lung model, for improved patient phenotyping and hence patient-specific therapy. An hypothesis motivating this research is that lung phenotypes may exhibit similar features by gender, age, and (normal or diseased) state, thus they can be clustered into sub- populations, and the structural and functional features in sub-populations may correlate with deposition of inhaled particulates and inflammation in the lungs. To achieve the goal and test the hypothesis, we propose the following specific aims. (1) Perform statistical analysis of airway image-based measurements and associated covariates. (2) Perform image registration analysis to study regional ventilation, tissue fraction and lung deformation. (3) Develop multi-scale subject-specific airway tree modeling and meshing algorithms for diseased lungs. (4) Apply a parallel CFD model to study airway resistance, particle deposition, and hot spots. Hot spots are the locations where inhaled particles, toxins, irritants, or bacteria accumulate in the lungs. (5) Seek supportive data from human studies to demonstrate that CFD modeling predicts lung regions susceptible to inflammation associated with enhanced deposition of inhaled particulate. We propose to analyze the existing and growing huge databases, such as lung computed tomography (CT) image data, demographic information, smoking history, and pulmonary function tests, collected by the NIH funded multi-center trials. Statistical methods will be applied to cluster and classify large data sets into sub-populations. The novelty of our approach lies in fusion of both static structural and dynamic functional phenotypes into our statistical analyses, including morphologic and topological airway measurements and threshold-based measurements of air trapping and emphysema extracted from a single CT lung image, deformation-based functional variables derived from image registration of CT images at two lung volumes, and CFD-predicted sensitive functional variables. These statistical tools will identify statistically significant phenotypes contrasting normal, COPD and asthmatic subjects, and identify a few subjects representative of sub-populations for multi-scale high- performance parallel CFD simulations to study flows, resistance, and hot spots, and their correlations with the inflammations of airways and tissues. Human subject studies will be conducted using volumetric 3D lung dual energy computed tomography (DECT) and 99mTc-MPAO-labelled white blood cell (WBC) lung SPECT imaging for model validation and longitudinal studies.

描述（由申请人提供）：该研究的最终目标是通过多尺度计算流体动力学将群体数据的统计分析与个体受试者的功能预测相结合，建立一个新的计算框架来评估和预测肺功能(CFD) 肺模型，用于改善患者表型分析，从而改善患者特异性治疗。推动这项研究的一个假设是，肺表型可能在性别、年龄和（正常或患病）状态方面表现出相似的特征，因此它们可以聚集成亚群，并且亚群中的结构和功能特征可能与沉积相关吸入颗粒物和肺部炎症。为了实现目标并检验假设，我们提出以下具体目标。 (1) 对基于气道图像的测量值和相关协变量进行统计分析。 (2) 进行图像配准分析以研究区域通气、组织分数和肺变形。 (3) 开发针对患病肺部的多尺度特定主题气道树建模和网格划分算法。 (4) 应用并行CFD模型研究气道阻力、颗粒沉积和热点。热点是吸入颗粒、毒素、刺激物或细菌在肺部积聚的位置。 (5) 从人体研究中寻求支持性数据，以证明 CFD 模型可以预测肺部区域易受与吸入颗粒沉积增强相关的炎症的影响。我们建议分析现有的和不断增长的庞大数据库，例如由 NIH 资助的多中心试验收集的肺计算机断层扫描 (CT) 图像数据、人口统计信息、吸烟史和肺功能测试。统计方法将 可应用于对大型数据集进行聚类并将其分类为子群体。我们方法的新颖之处在于将静态结构和动态功能表型融合到我们的统计分析中，包括形态学和拓扑气道测量以及从单个 CT 肺部图像中提取的空气滞留和肺气肿的基于阈值的测量，基于变形的功能变量来自两个肺体积的 CT 图像的图像配准以及 CFD 预测的敏感功能变量。这些统计工具将识别与正常、慢性阻塞性肺病和哮喘受试者相比具有统计显着性的表型，并识别代表子群体的一些受试者，用于多尺度高性能并行 CFD 模拟，以研究流量、阻力和热点及其与这 气道和组织炎症。将使用体积 3D 肺双能计算机断层扫描 (DECT) 和 99mTc-MPAO 标记的白细胞 (WBC) 肺 SPECT 成像进行人体受试者研究，以进行模型验证和纵向研究。