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整合统计分析，通过将人口数据的统计分析与个体尺度计算流体动力学（CFD）肺模型整合到单个受试者中的统计分析，以建立一个新的计算框架，以评估和预测肺功能的功能，以改善患者表型和患者特定治疗。促使这项研究的一个假设是，肺表型可能表现出性别，年龄和（正常或患病）状态的相似特征，因此可以将它们聚集到亚种群中，并且亚群中的结构和功能性特征可能与吸入的颗粒物和肺部炎症的下降相关。为了实现目标并检验假设，我们提出了以下特定目标。 （1）对基于气道图像的测量和相关的协变量进行统计分析。 （2）执行图像注册分析以研究区域通风，组织分数和肺部变形。 （3）为患病的肺部开发多尺度主体特异性气道树建模和网格划分算法。 （4）应用平行的CFD模型来研究气道电阻，颗粒沉积和热点。热点是吸入颗粒，毒素，刺激性或细菌在肺部积聚的位置。 （5）从人类研究中寻求支持性数据，以证明CFD建模预测肺区域容易受到与吸入颗粒物增强沉积相关的炎症。我们建议分析现有的巨大数据库，例如肺计算机断层扫描（CT）图像数据，人口统计信息，吸烟史和肺功能测试，该数据库由NIH资助的多中心试验收集。统计方法将 应用于群集并将大型数据集分类为子人群。 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.这些统计工具将确定与正常，COPD和哮喘受试者对比的统计学上重要的表型，并确定一些代表多规模高性能平行的子人群的主题，以研究流动，阻力和热点，以及它们与它们与它们与它们的相关性的相关性，及其与它们的相关性以及它们的相关性 气道和组织的炎症。人类学科研究将使用体积3D肺双能计算机断层扫描（DECT）和99MTC-MPAO标记的白细胞（WBC）肺Spect成像进行模型验证和纵向研究。