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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

基本信息

批准号：
9283608
负责人：
CHING-LONG LIN
金额：
$ 63.14万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-15 至 2020-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9283608
关键词：
Age Air Airway Resistance Algorithms Animals Asthma Back Bacteria Breathing Caliber Chronic Obstructive Airway Disease Classification Cluster Analysis Data Data Analyses Data Set Databases Deposition Disease Environmental air flow Exhibits Funding Gender Goals Hot Spot Image Individual Inflammation Iowa Irritants Label Length Letters Leukocytes Liquid substance Lobe Location Longitudinal Studies Lung Lung diseases Measurement Modeling Morphology Multicenter Trials Outcome Measure Particulate Patients Performance Phenotype Photons Population Population Analysis Population Study Pulmonary Emphysema Pulmonary function tests Radiology Specialty Research Resistance Respiratory physiology Rotation Smoking History Statistical Data Interpretation Statistical Methods Stress Structure-Activity Relationship Study Subject Techniques Testing Thick Tissues Toxin Trees United States National Institutes of Health University Hospitals X-Ray Computed Tomography airway inflammation asthmatic asthmatic airway base computer cluster computer framework data modeling density experimental study genetic epidemiology high dimensionality human data human subject image guided image registration improved lung imaging lung volume parallel computer particle phenotypic biomarker predictive modeling programs public health relevance simulation single photon emission computed tomography statistics tool validation studies

项目摘要

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）：本研究的最终目标是通过多尺度计算流体动力学（CFD）肺模型将人群数据的统计分析与个体受试者的功能预测相结合，建立一个新的肺功能评估和预测计算框架，以改善患者表型，从而改善患者特异性治疗。激发该研究的假设是，肺表型可以根据性别、年龄和（正常或患病）状态表现出相似的特征，因此它们可以聚类成亚群，并且亚群中的结构和功能特征可以与吸入颗粒的沉积和肺部炎症相关。为了实现这一目标并检验这一假设，我们提出了以下具体目标。(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，以研究局部通气、组织分数和肺变形。(3)为患病肺部开发多尺度受试者特定气道树建模和网格化算法。(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建模预测肺部区域易受与吸入颗粒物沉积增强相关的炎症影响。我们建议分析现有的和不断增长的巨大数据库，如肺计算机断层扫描（CT）图像数据，人口统计学信息，吸烟史和肺功能测试，收集由NIH资助的多中心试验。统计方法将将大型数据集聚类和分类为子群体。我们的方法的新奇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，包括形态和拓扑气道测量以及从单个CT肺图像中提取的空气捕获和肺气肿的基于阈值的测量，来自两个肺体积的CT图像的图像配准的基于变形的功能变量，以及CFD预测的敏感功能变量。这些统计工具将识别对比正常、COPD和哮喘受试者的统计学显著表型，并识别代表多尺度高性能并行CFD模拟的亚群的少数受试者，以研究流动、阻力和热点，以及它们与气道和组织的炎症。人体受试者研究将使用体积3D肺双能计算机断层扫描（DECT）和99 mTc-MPAO标记的白色血细胞（WBC）肺SPECT成像进行模型验证和纵向研究。