Automatically generated, objective, and reproducible CT imaging-based biomarkers are critical for the differen- tiation of sub-classes of pulmonary disease to better link phenotypes to genotypes, enabling the development of new treatments for lung diseases like chronic obstructive pulmonary disease (COPD), interstitial pulmonary fibrosis, sarcoidosis, or asthma. While previous research on developing image-based biomarkers for COPD and asthma has shown promising results, the translation of already developed CT biomarkers for disease entities with high radiodensity pathology caused by significant inflammation, consolidation, alveolar flooding or fibro- sis was not successful up to now due to the lack of automated robust lung image analysis techniques. This shortcoming not only hinders the utilization of existing CT biomarkers, it is also problematic for developing new image-based biomarkers for these lung diseases. The objective of this proposal is to address this bottleneck in quantitative lung image analysis by developing robust and fast image analysis techniques for single CT scan and high/low volume CT scan pairs. Specifically, novel methods for lung, lung lobe, and airway segmentation will be developed and validated, which can tolerate high density lung pathology and are a key component required for calculating CT biomarkers for lung pathoanatomy. Robustness of lung and lobe segmentation methods will be achieved by utilizing model-based image analysis methods. Up to now, such approaches were considered as too computationally demanding for lung image analysis because of the large organ size. We will address this issue by utilizing general-purpose computation on graphics processing hardware techniques, allowing us to reduce computation times significantly, as demonstrated by preliminary results. By providing an efficient means of objectively identifying lung structures, these structures can be quantified and utilized for sub-phenotyping patient populations, as required to facilitate large multi-center studies with 20,000 or more subjects.

Public Health Relevance

Relevant to Public Health Automated lung CT image analysis technology developed within this project will enable utilizing objective image- based biomarkers for lung diseases like interstitial pulmonary fibrosis, sarcoidosis, severe asthma, or COPD in large multi-center studies.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL111453-03
Application #
8604409
Study Section
Special Emphasis Panel (ZRG1-BMIT-J (01))
Program Officer
Gan, Weiniu
Project Start
2012-01-01
Project End
2014-12-31
Budget Start
2014-01-01
Budget End
2014-12-31
Support Year
3
Fiscal Year
2014
Total Cost
$339,750
Indirect Cost
$114,750
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
Bauer, Christian; Eberlein, Michael; Beichel, Reinhard R (2015) Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts. IEEE Trans Med Imaging 34:1063-76
Gill, Gurman; Bauer, Christian; Beichel, Reinhard R (2014) A method for avoiding overlap of left and right lungs in shape model guided segmentation of lungs in CT volumes. Med Phys 41:101908
Gill, Gurman; Toews, Matthew; Beichel, Reinhard R (2014) Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach. Int J Biomed Imaging 2014:479154
Sun, Shanhui; Sonka, Milan; Beichel, Reinhard R (2013) Graph-based IVUS segmentation with efficient computer-aided refinement. IEEE Trans Med Imaging 32:1536-49
Sun, Shanhui; Sonka, Milan; Beichel, Reinhard R (2013) Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface. Comput Med Imaging Graph 37:15-27
Adam, Ryan J; Michalski, Andrew S; Bauer, Christian et al. (2013) Air trapping and airflow obstruction in newborn cystic fibrosis piglets. Am J Respir Crit Care Med 188:1434-41