Search

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. 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. 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. 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. • CFD simulation of breathing with prescribed wall motion derived from MRI • Different results are achieved with static versus dynamic simulations. • Peak differences: resistance (19.8%); flow division (13.0%); pressure loss (19.2%) • Airway motion is found to be out of phase with breath. • Power transfer between air and airway wall is calculated. [ABSTRACT FROM AUTHOR]

Esophageal atresia (EA) is a relatively uncommon congenital anomaly, often observed in conjunction with tracheoesophageal fistula (TEF). Surgical repair in neonates typically takes place with little information about the pre-existing EA/TEF structure because there are currently no acceptable tools for evaluating EA/TEF anatomy prior to repair; chest x-ray radiograph does not identify malformation sub-type or gap length, while x-ray computed tomography (CT) demonstrates an unacceptably high exposure to ionizing radiation. There is a need for safe imaging methods to evaluate pre-operative EA/TEF anatomy, which would add value in surgical planning; this need may be met with high-resolution structural MRI. We report three cases of Type-C EA/TEF in neonates. Patients were imaged prior to surgical repair using high-resolution ultrashort echo time (UTE) magnetic resonance imaging (MRI) to visualize tracheoesophageal anatomy and allow for informed surgical planning and risk management. One of the three patients was imaged post-repair to evaluate surgical efficacy and evolution of the tracheoesophageal anatomy. [ABSTRACT FROM AUTHOR]

Modeling pulmonary drug delivery in the airway using computational fluid dynamics (CFD) simulations tracks drug particles throughout the airway, providing valuable information on the deposition location of inhaled drugs. However, most studies simulate particle transport within static airway models that do not incorporate physiological airway motion; this choice limits accuracy since airway motion directly affects particle transport and deposition, notably in newborns with airway abnormalities such as tracheomalacia. The objective of this study is to determine the effect of airway motion on drug delivery in neonates with and without airway disease. For this study, two control subjects without any airway disease and three subjects with tracheomalacia (dynamic tracheal narrowing) were enrolled. Each subject was imaged at approximately 40-weeks post-menstrual age using magnetic resonance imaging (MRI). MRI data were retrospectively reconstructed to obtain static airway images gated to different time points of the breath (i.e., end expiration and end inspiration) and an image representing combined data from all timepoints (ungated). Virtual airway surfaces (pharynx to main bronchi) were made from each MR image. A moving airway surface was created from surface registration of these surfaces and used as the boundary for a CFD simulation of one inhalation, along with subject-specific inspiratory flow waveforms. To assess the effect of airway wall motion on particle deposition, static-walled simulations, based on the airway surfaces at end inspiration, end expiration, and the ungated airway surface, were also performed using the same flow boundary conditions. Particle transport (particles diameter range 0.5–15 μm) was compared between the simulations during the inhalation. Airway surface motion affected particle transport into the small airways by 65% on average (0.5–5 μm– 22%, 5-15 μm– 86%) compared to static-walled simulations, while comparison between static end expiration and other static-walled simulations using geometries acquired during different phases of breathing differed by more than 500% on average (0.5–5 μm– 45%, 5-15 μm– 741%). For particle deposition, airway surface motion affected by 43% on average (0.5–5 μm– 86%, 5-15 μm– 21%) compared to static-walled simulations and comparison between static end expiration and other static-walled simulations differed by 47% on average (0.5–5 μm– 58%, 5-15 μm– 41%). Differences between dynamic and static deposition results and between static simulations from different timepoints occurred in patients with and without airway disease. This study suggests the importance of using airway wall motion in CFD simulations to model aerosolized drug delivery in the airway. If a CFD simulation is limited to only a static airway image without physiological motion, particle deposition mapping may yield markedly inaccurate results, potentially resulting in higher or lower drug dosing than intended. • Airway motion affects the particle transport and delivery in the airway. • Particle simulation in the dynamic airway differed from static simulations by 65%. • Pulmonary drug delivery simulations depend on the breathing phase of medical imaging. • Particle delivery in static simulations varied >500% compared to end expiration phase. [ABSTRACT FROM AUTHOR]