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Clin Biomech (Bristol, Avon). 2019 Jun;66:88-96. doi: 10.1016/j.clinbiomech.2017.10.011. Epub 2017 Oct 14.

Assessing the relationship between movement and airflow in the upper airway using computational fluid dynamics with motion determined from magnetic resonance imaging.

Author information

1
Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Bioengineering, Imperial College London, UK. Electronic address: alister.bates@cchmc.org.
2
Department of Computing, Imperial College London, UK.
3
Siemens Industry Software Computational Dynamics Ltd, London, UK.
4
Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
5
Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
6
Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
7
Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.

Abstract

BACKGROUND:

Computational fluid dynamics simulations of respiratory airflow in the upper airway reveal clinically relevant information, including sites of local resistance, inhaled particle deposition, and the effect of pathological constrictions. Unlike previous simulations, which have been performed on rigid anatomical models from static medical imaging, this work utilises ciné imaging during respiration to create dynamic models and more closely represent airway physiology.

METHODS:

Airway movement maps were obtained from non-rigid image registration of fast-cine MRI and applied to high-spatial-resolution airway surface models. Breathing flowrates were recorded simultaneously with imaging. These data formed the boundary conditions for large eddy simulation computations of the airflow from exterior mask to bronchi. Simulations with rigid geometries were performed to demonstrate the resulting airflow differences between airflow simulations in rigid and dynamic airways.

FINDINGS:

In the analysed rapid breathing manoeuvre, incorporating airway movement significantly changed the findings of the CFD simulations. Peak resistance increased by 19.8% and occurred earlier in the breath. Overall pressure loss decreased by 19.2%, and the proportion of flow in the mouth increased by 13.0%. Airway wall motion was out-of-phase with the air pressure force, demonstrating the presence of neuromuscular motion. In total, the anatomy did 25.2% more work on the air than vice versa.

INTERPRETATIONS:

Realistic movement of the airway is incorporated into CFD simulations of airflow in the upper airway for the first time. This motion is vital to producing clinically relevant computational models of respiratory airflow and will allow novel analysis of dynamic conditions, such as sleep apnoea.

KEYWORDS:

CFD; Image registration; MRI; Movement; Sleep apnoea; Upper airways

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