Chronic obstructive pulmonary disease (COPD) is a growing health concern associated with high morbidity and mortality, and is currently the third-ranked cause of death in the United States. COPD is characterized by airflow limitation that is not fully reversible and includes chronic bronchitis, functional small airway disease, and emphysema. The interrelationship between emphysema and airway disease in COPD makes it a highly complex and heterogeneous disorder. Appropriate diagnosis of COPD is vital to administer targeted therapy strategies that can improve patient’s quality of life and reduce the frequency of COPD associated exacerbations. Although spirometry or pulmonary function tests are currently the gold standard for the diagnosis and staging of the disease, their lack of reproducibility and minimal information on regional characterization of the lung tissue destruction makes it hard to rely on to phenotype COPD population and predict disease progression. Quantification of COPD, as done by computed tomography (CT) methods has seen significant advancements, helping us understand the complex pathophysiology of this disease. The prospective and established techniques that are derived from CT imaging such as densitometry, texture, airway, and pulmonary vasculature-based analyses have been successful in regional characterization of emphysema related lung tissue destruction and airway disease related morphological changes in COPD patients. Although, these measures enriched our diagnostic and treating capability of COPD, they lack information on patient specific alterations in lung mechanics and regional parenchymal stresses. This valuable information can be achieved through the use of image registration protocols. Our main goal of this research work is to examine and evaluate the role of lung mechanical measures derived from CT image registration techniques in COPD diagnosis, phenotyping, and progression.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-6834 |
| Date | 01 December 2016 |
| Creators | Bodduluri, Sandeep |
| Contributors | Reinhardt, Joseph M. |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | dissertation |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright © 2016 Sandeep Bodduluri |
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