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.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZEB1-OSR-C (M1))
Program Officer
Gan, Weiniu
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Iowa
Engineering (All Types)
Schools of Engineering
Iowa City
United States
Zip Code
Miyawaki, Shinjiro; Tawhai, Merryn H; Hoffman, Eric A et al. (2016) Automatic construction of subject-specific human airway geometry including trifurcations based on a CT-segmented airway skeleton and surface. Biomech Model Mechanobiol :
Chan, Kung-Sik; Jiao, Feiran; Mikulski, Marek A et al. (2016) Novel Logistic Regression Model of Chest CT Attenuation Coefficient Distributions for the Automated Detection of Abnormal (Emphysema or ILD) Versus Normal Lung. Acad Radiol 23:304-14
Hedges, Kerry L; Tawhai, Merryn H (2016) Simulation of Forced Expiration in a Biophysical Model, With Homogeneous and Clustered Bronchoconstriction. J Biomech Eng 138:061008
Chen, Kun; Chan, Kung-Sik (2016) A note on rank reduction in sparse multivariate regression. J Stat Theory Pract 10:100-120
Chen, Kun (2016) Model diagnostics in reduced-rank estimation. Stat Interface 9:469-484
Hung, Hung; Lin, Yu-Ting; Chen, Penweng et al. (2016) Detection of gene-gene interactions using multistage sparse and low-rank regression. Biometrics 72:85-94
Ellingwood, Nathan D; Yin, Youbing; Smith, Matthew et al. (2016) Efficient methods for implementation of multi-level nonrigid mass-preserving image registration on GPUs and multi-threaded CPUs. Comput Methods Programs Biomed 127:290-300
Luo, Chongliang; Liu, Jin; Dey, Dipak K et al. (2016) Canonical variate regression. Biostatistics 17:468-83
Choi, Sanghun; Hoffman, Eric A; Wenzel, Sally E et al. (2015) Quantitative assessment of multiscale structural and functional alterations in asthmatic populations. J Appl Physiol (1985) 118:1286-98
Jahani, Nariman; Choi, Sanghun; Choi, Jiwoong et al. (2015) Assessment of regional ventilation and deformation using 4D-CT imaging for healthy human lungs during tidal breathing. J Appl Physiol (1985) 119:1064-74

Showing the most recent 10 out of 16 publications