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.

Public Health Relevance

This proposal aims to develop a new computational framework that integrates an individualized multi-scale lung model with image registration, geometric modeling and statistical analyses, to study structure-function relationships over a population of normal, asthmatic and COPD subjects.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01HL114494-02
Application #
8714034
Study Section
Special Emphasis Panel (ZEB1-OSR-C (M1))
Program Officer
Gan, Weiniu
Project Start
2013-08-15
Project End
2018-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
2
Fiscal Year
2014
Total Cost
$618,221
Indirect Cost
$168,771
Name
University of Iowa
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
062761671
City
Iowa City
State
IA
Country
United States
Zip Code
52242
Haghighi, Babak; Choi, Sanghun; Choi, Jiwoong et al. (2018) Imaging-based clusters in current smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS). Respir Res 19:178
Haghighi, Babak; D Ellingwood, Nathan; Yin, Youbing et al. (2018) A GPU-based symmetric non-rigid image registration method in human lung. Med Biol Eng Comput 56:355-371
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Choi, Sanghun; Miyawaki, Shinjiro; Lin, Ching-Long (2018) A Feasible Computational Fluid Dynamics Study for Relationships of Structural and Functional Alterations with Particle Depositions in Severe Asthmatic Lungs. Comput Math Methods Med 2018:6564854
Chen, Kun; Mishra, Neha; Smyth, Joan et al. (2018) A Tailored Multivariate Mixture Model for Detecting Proteins of Concordant Change Among Virulent Strains of Clostridium Perfringens. J Am Stat Assoc 113:546-559
Choi, Sanghun; Haghighi, Babak; Choi, Jiwoong et al. (2017) Differentiation of quantitative CT imaging phenotypes in asthma versus COPD. BMJ Open Respir Res 4:e000252
Yu, Chun; Yao, Weixin; Chen, Kun (2017) A new method for robust mixture regression. Can J Stat 45:77-94

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