Infants and children commonly suffer from structural abnormalities of the upper airway that result in insufficient respiration and problems ranging from simple obstructive sleep apnea to imminent, life- threatening airway obstruction. In many cases dynamic airway collapse significantly contributes to airway obstruction. Unfortunately, methods for quantitative assessment of the dynamic airway are lacking. Quantitative imaging and computational modeling of the dynamic airway could prove very beneficial not only for the diagnosis of airway obstruction but also for aiding in medical and surgical decision-making. These new tools and methods could also be utilized for predictive modeling and subsequent treatment planning. Current quantitative imaging methods, including MRI and CT, have several limitations. Infants and young children may need to be sedated for long scan times, which can be quite hazardous for patients with airway obstruction. In the case of CT, there are risks associated with ionizing radiation exposure. These methods also do not readily offer real-time imaging to study airway dynamics. Thus, airway endoscopy (laryngoscopy and bronchoscopy) remains the gold standard for the evaluation of airway obstruction. However, endoscopy is only semi-quantitative as it cannot accurately measure the luminal cross-sectional area (CSA), the primary parameter affecting airflow resistance. We propose to address the need for quantitative, real-time, dynamic upper airway imaging by developing a technology based upon anatomic Optical Coherence Tomography (aOCT) delivered via standard bronchoscopy. This will enable 3D computational models of the airway for accurate computational fluid dynamic (CFD) modeling. Our first Specific Aim will be to validate aOCT in cadaveric pigs as a functionally equivalent substitute for CT for creating 3D virtual airway geometries for CFD. Our hypothesis is that the upper airway luminal geometries obtained by bronchoscopic aOCT can predict equivalent air flow resistance as that obtained by CT. As an exploratory aspect of this Aim, we will also perform imaging of adult human cadaveric lungs to evaluate the capability for quantitative imaging beyond the carina into the mainstem, segmental, and smaller bronchi. Our second Specific Aim will be to incorporate technological advances into the aOCT system to enable dynamic, real-time (3+1) dimensional imaging to capture phases of the respiratory cycle in an in vivo pig model. Our third Specific Aim will be to perform elastography of the airway wall by collecting simultaneous in situ pressure and aOCT imaging data of pigs in vivo to during spontaneous respiration and while under controlled, variable airway pressure. This data will inform a dynamic flexible airway model for CFD computations that model the fluid-structure interaction (FSI). These pre-clinical steps of validation, technological advancement, and association with physiologic parameters of obstructive airway diseases will position the aOCT technology for rapid translation to clinical airway imaging in humans.

Public Health Relevance

There is a deficiency of real-time, accurate biomedical imaging technologies with data that is adaptable to modern computational methods with predictive value available to inform clinicians about airway obstruction, an extraordinarily common clinical problem. This proposal will lead to a readily adaptable, minimally- invasive, endoscopic optical imaging method for accurately measuring the airway in real-time, capturing the breathing cycle during various states of consciousness, sedation, or anesthesia. Pre-clinical in vivo animal studies are proposed to advance the technology and determine its applicability toward measuring and correlating physiological parameters utilized to assess airway obstruction.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL123557-01A1
Application #
8886082
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Gan, Weiniu
Project Start
2015-09-01
Project End
2018-08-31
Budget Start
2015-09-01
Budget End
2016-08-31
Support Year
1
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Physics
Type
Schools of Arts and Sciences
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
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