Chamindu C Gunatilaka1, Andreas Schuh2, Nara S Higano3, Jason C Woods4, Alister J Bates5. 1. Center for Pulmonary Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, USA; Department of Physics, University of Cincinnati, Cincinnati, USA. 2. Department of Computing, Imperial College London, London, UK. 3. Center for Pulmonary Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, USA; Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, USA. 4. Center for Pulmonary Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, USA; Department of Physics, University of Cincinnati, Cincinnati, USA; Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, USA; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, USA. 5. Center for Pulmonary Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, USA; Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, USA. Electronic address: Alister.Bates@cchmc.org.
Abstract
RATIONALE: Computational fluid dynamics (CFD) simulations of respiratory airflow can quantify clinically useful information that cannot be obtained directly, such as the work of breathing (WOB), resistance to airflow, and pressure loss. However, patient-specific CFD simulations are often based on medical imaging that does not capture airway motion and thus may not represent true physiology, directly affecting those measurements. OBJECTIVES: To quantify the variation of respiratory airflow metrics obtained from static models of airway anatomy at several respiratory phases, temporally averaged airway anatomies, and dynamic models that incorporate physiological motion. METHODS: Neonatal airway images were acquired during free-breathing using 3D high-resolution MRI and reconstructed at several respiratory phases in two healthy subjects and two with airway disease (tracheomalacia). For each subject, five static (end expiration, peak inspiration, end inspiration, peak expiration, averaged) and one dynamic CFD simulations were performed. WOB, airway resistance, and pressure loss across the trachea were obtained for each static simulation and compared with the dynamic simulation results. RESULTS: Large differences were found in the airflow variables between the static simulations at various respiratory phases and the dynamic simulation. Depending on the static airway model used, WOB, resistance, and pressure loss varied up to 237%, 200%, and 94% compared to the dynamic simulation respectively. CONCLUSIONS: Changes in tracheal size and shape throughout the breathing cycle directly affect respiratory airflow dynamics and breathing effort. Simulations incorporating realistic airway wall dynamics most closely represent airway physiology; if limited to static simulations, the airway geometry must be obtained during the respiratory phase of interest for a given pathology.
RATIONALE: Computational fluid dynamics (CFD) simulations of respiratory airflow can quantify clinically useful information that cannot be obtained directly, such as the work of breathing (WOB), resistance to airflow, and pressure loss. However, patient-specific CFD simulations are often based on medical imaging that does not capture airway motion and thus may not represent true physiology, directly affecting those measurements. OBJECTIVES: To quantify the variation of respiratory airflow metrics obtained from static models of airway anatomy at several respiratory phases, temporally averaged airway anatomies, and dynamic models that incorporate physiological motion. METHODS: Neonatal airway images were acquired during free-breathing using 3D high-resolution MRI and reconstructed at several respiratory phases in two healthy subjects and two with airway disease (tracheomalacia). For each subject, five static (end expiration, peak inspiration, end inspiration, peak expiration, averaged) and one dynamic CFD simulations were performed. WOB, airway resistance, and pressure loss across the trachea were obtained for each static simulation and compared with the dynamic simulation results. RESULTS: Large differences were found in the airflow variables between the static simulations at various respiratory phases and the dynamic simulation. Depending on the static airway model used, WOB, resistance, and pressure loss varied up to 237%, 200%, and 94% compared to the dynamic simulation respectively. CONCLUSIONS: Changes in tracheal size and shape throughout the breathing cycle directly affect respiratory airflow dynamics and breathing effort. Simulations incorporating realistic airway wall dynamics most closely represent airway physiology; if limited to static simulations, the airway geometry must be obtained during the respiratory phase of interest for a given pathology.
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