This project aims to create a validated computational tool to predict surgical outcomes for pediatric patients with obstructive sleep apnea (OSA). OSA is a common condition, affecting 2.2 million children in the USA alone. It is characterized as upper airway obstruction during sleep, which causes disrupted sleep and leads to developmental delay, cardiovascular complications and impaired growth. The first line of treatment for children with OSA is to remove their tonsils and adenoids; however, these surgeries do not always cure the patient. Another treatment, continuous positive airway pressure (CPAP) is only tolerated by 50% of children. Therefore, many children undergo surgical interventions 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 clear need for a tool to improve the efficacy of these surgeries and predict which of the various surgical options is going to 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. Previous CFD simulations have been unable to provide information about OSA as they were based on rigid geometries, or did not include neuromuscular motion, a key component in OSA. This project uses real-time magnetic resonance imaging (MRI) to provide the anatomy and motion of the airway to the CFD simulation, meaning that the exact in vivo motion is modeled for the first time. Furthermore, since the modeling is based on MRI, a modality which does not use ionizing radiation, it is suitable for longitudinal assessment of patients before and after surgical procedures. In vivo validation of these models will be achieved for the first time through comparison of CFD-based airflow velocity fields with those generated by phase-contrast MRI of inhaled hyperpolarized 129Xe gas. Cincinnati Children’s Hospital is a world leader in the fields of pediatric pulmonary and sleep medicine and radiology, and is the ideal environment to conduct the proposed research and for the PI to develop the essential skills to have a successful career as an independent investigator. The PI has identified primary mentors in both technical and clinical fields, and a further mentoring team to assist with specific aspects of this project. Between them, they have experience in MRI, sleep medicine, pulmonary medicine, CFD modeling, airway surgery, radiology, and biostatistics, as well as career development through this award mechanism. Their knowledge strengthens and complements the PI’s background in computational modeling o...