Chronic lower respiratory disease, primarily Chronic Obstructive Pulmonary Disease (COPD), was the third leading cause of death in the United States in 2011. COPD is characterized by damaged lung tissues that alter their biomechanical properties and ventilation profiles. Current staging methods using pulmonary function tests are insufficient to determine COPD phenotypes and the subsequent treatments may not be as effective as possible. New markers are needed to better characterize the full clinical spectrum of the disease and to guide the development and assessment of new and more effective therapies. The goal of this proposal is to develop a method for improving COPD phenotype characterization and response monitoring through a novel patient- specific flow structure interaction (FSI) model, generated using data acquired during free breathing. Although biomechanical properties and airflow dynamics of COPD patients are direct indicators of the phenotype, little work has been done to non-invasively measure or model them in a subject-specific manner.
In Aim 1, we will develop a novel low dose process for acquiring fast helical free breathing lung CT images and improving the spatial accuracy of deformable image registration, a process that is critical to the subsequent measurement and characterization of biomechanical lung tissue properties. Current methods are unable to distinguish the fine differences in motion between differing lung structures. The proposed approach takes advantage of the quantitative nature of computed tomography, as well as a recently developed fast helical free breathing CT protocol to provide deformable image registration of unparalleled quality and resolution.
In Aim 2, we will employ these registrations, along with fundamental biomechanical principles, to measure the hyperelastic properties of the subject's lungs. Hyperelastic property descriptions allow for nonlinear elastic behavior. Previous work has focused on linear elastic modeling. Our hypothesis is that a hyperelastic model will better characterize diseased tissues.
In Aim 3, we will employ computational fluid dynamics techniques to characterize the subject's airflow dynamics. They will be combined with the hyperelastic biomechanical properties measured in SA2 to create the FSI model.
In Aim 4, we will conduct a pilot clinical trial enrolling radiation therapy patients with and without COPD, where a bronchodilator will be administered between two CT scanning sessions. The subjects will also receive standard pulmonary functional tests. Our hypothesis is that the FSI model will identify COPD patients from normal lung subjects, and characterize COPD phenotypes better than clinical functional tests, opening the way for research into personalized medicine for these patients to improve their clinical outcomes.
A novel imaging biomarker will be developed that simultaneously incorporates lung tissue elasticity estimations and airflow dynamics to more accurately characterize COPD. This improved characterization will enable researchers to develop personalized treatments that lead to better treatment response and improved quality of life.
