Computational Fluid Dynamics and Airway Research

Our research focuses on human airways and how they change in various disease conditions. We research airway behavior in children with obstructive sleep apnea (OSA) and premature babies born with tracheomalacia (TM) and congenital abnormalities. Our goals are to identify how airway problems affect patients’ symptoms, inform, and evaluate surgical and therapeutic interventions, and differentiate the effects of airway abnormalities versus lung disease.

Our researchers have the world's first virtual models of human airways that move realistically base the motion on high-speed magnetic resonance imaging (MRI).

The effects of airway diseases are difficult to measure in patients and deciphering which treatments will be effective. We create virtual models of airways from MRIs. We then use computational fluid dynamics (CFD) to simulate how air flows through the airway. This model shows us where in the airway are regions with high resistance. We can virtually alter the airway to predict how it would change after treatment. We also calculate the effect that treatment would have on airway symptoms. Our goal is to predict the best treatment approach for children with OSA and premature babies with TM.

Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is characterized by recurrent partial or complete collapse of the upper airway during sleep. Each collapse limits airflow, causing derangement in gas exchange and recurrent arousals from sleep which can lead to behavioral problems, learning difficulties or more serious medical issues.

Continuous positive airway pressure (CPAP) is the first line treatment for OSA; however, adherence in CPAP use is a major obstacle. Hypoglossal nerve stimulation (HGNS) is a treatment option for patients with obstructive sleep apnea who are unable to tolerate continuous positive airway pressure.

Our researchers are currently investigating HGNS in a clinical trial. The purpose of this study is to observe how the HGNS affects the airway in patients who do and don’t respond to the device. The outcome will be the identification of factors that cause patients to be non-responders. Our group has developed a unique capability to measure upper airway muscle tone via analysis of the upper airway motion (derived from CT) and the air pressure forces which act on the airway wall (derived from CFD simulations). This study will accomplish this by imaging the upper airway during natural sleep, geometric analysis of airway anatomy and motion and airflow modeling in the moving airway.

Additionally, we have a clinical trial that aims to create a validated computational tool to predict surgical outcomes for pediatric patients with OSA. These surgical interventions are aimed at soft tissue structures surrounding the airway, such as tonsils, tongue, and soft palate, and/or the bony structures of the face. However, the success rates of these surgeries, measured as a reduction in the obstructive apnea-hypopnea index (obstructive events per hour of sleep), is surprisingly low. Therefore, there is a need for a tool to improve the efficacy of these surgeries and predict which of the various surgical options will benefit each individual patient most effectively. Computational fluid dynamics (CFD) simulations of respiratory airflow in the upper airways can provide this predictive tool, allowing the effects of various surgical options to be compared virtually and the option most likely to improve the patient’s condition to be chosen.