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

描述(申请人提供)：这项研究的最终目标是通过多尺度计算流体动力学(CFD)肺模型将人口数据的统计分析与个体受试者的功能预测整合在一起，建立一个新的计算框架来评估和预测肺功能，以改进患者的表型，从而改善患者的特定治疗。支持这项研究的一个假设是，肺的表型可能因性别、年龄和(正常或患病)状态而表现出相似的特征，因此它们可以被聚为亚群，并且亚群中的结构和功能特征可能与吸入颗粒物的沉积和肺部的炎症相关。为了实现这一目标并检验假设，我们提出了以下具体目标。(1)对基于图像的气道测量和相关协变量进行统计分析。(2)进行图像配准分析，研究局部通气量、组织分数和肺变形。(3)针对病变肺，提出了多尺度特定对象的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。热点是吸入的颗粒、毒素、刺激物或细菌在肺部聚集的地方。(5)从人体研究中寻求支持性数据，以证明CFD建模预测与吸入颗粒物沉积增强相关的炎症易感区域。我们建议分析NIH资助的多中心试验收集的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、人口统计信息、吸烟史和肺功能测试。统计方法将 用于对大数据集进行聚类并将其分类为子总体。我们方法的创新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，包括从单个CT肺图像提取的空气滞留和肺气肿的形态和拓扑测量以及基于阈值的测量，从两个肺体积的CT图像配准得到的基于变形的函数变量，以及CFD预测的敏感函数变量。这些统计工具将识别与正常受试者、COPD受试者和哮喘受试者相比具有统计学意义的表型，并识别一些代表亚群的受试者，以进行多尺度高性能并行CFD模拟，以研究流动、阻力和热点，以及它们与 呼吸道和组织发炎。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99mTC-MPAO标记的白细胞(WBC)肺SPECT成像进行模型验证和纵向研究。

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.