Nasal decongestant reduces blood flow to the nasal turbinates, reducing tissue volume and increasing nasal airway patency. This study maps the changes in nasal anatomy and measures how these changes affect nasal resistance, flow partitioning between superior and inferior cavity, flow patterns and wall shear stress. High-resolution MRI was applied to capture nasal anatomy in 10 healthy subjects before and after application of a topical decongestant. Computational fluid dynamics simulated nasal airflow at steady inspiratory flow rates of 15 L.min$$^{-1}$$ - 1 and 30 L.min$$^{-1}$$ - 1 . The results show decongestion mainly increases the cross-sectional area in the turbinate region and SAVR is reduced (median approximately 40$$\%$$ % reduction) in middle and lower parts of the cavity. Decongestion reduces nasal resistance by 50$$\%$$ % on average, while in the posterior cavity, nasal resistance decreases by a median factor of approximately 3 after decongestion. We also find decongestant regularises nasal airflow and alters the partitioning of flow, significantly decreasing flow through the superior portions of the nasal cavity. By comparing nasal anatomies and airflow in their normal state with that when pharmacologically decongested, this study provides data for a broad range of anatomy and airflow conditions, which may help characterize the extent of nasal variability.

The energy needed to drive airflow through the trachea normally constitutes a minor component of the work of breathing. However, with progressive tracheal compression, patient subjective symptoms can include severe breathing difficulties. Many patients suffer multiple respiratory comorbidities and so it is important to assess compression effects when evaluating the need for surgery. This work describes the use of computational prediction to determine airflow resistance in compressed tracheal geometries reconstructed from a series of CT scans. Using energy flux analysis, the regions that contribute the most to airway resistance during inhalation are identified. The principal such region is where flow emerging from the zone of maximum constriction undergoes breakup and turbulent mixing. Secondary regions are also found below the tongue base and around the glottis, with overall airway resistance scaling nearly quadratically with flow rate. Since the anatomical extent of the imaged airway varied between scans-as commonly occurs with clinical data and when assessing reported differences between research studies-the effect of sub-glottic inflow truncation is considered. Analysis shows truncation alters the location of jet breakup and weakly influences the pattern of pressure recovery. Tests also show that placing a simple artificial glottis in the inflow to a truncated model can replicate patterns of energy loss in more extensive models, suggesting a means to assess sensitivity to domain truncation in tracheal airflow simulations.

Computational fluid dynamics (CFD) simulations of respiratory airflow have the potential to change the clinical assessment of regional airway function in health and disease, in pulmonary medicine and otolaryngology. For example, in diseases where multiple sites of airway obstruction occur, such as obstructive sleep apnea (OSA), CFD simulations can identify which sites of obstruction contribute most to airway resistance and may therefore be candidate sites for airway surgery. The main barrier to clinical uptake of respiratory CFD to date has been the difficulty in validating CFD results against a clinical gold standard. Invasive instrumentation of the upper airway to measure respiratory airflow velocity or pressure can disrupt the airflow and alter the subject’s natural breathing patterns. Therefore, in this study, we instead propose phase contrast (PC) velocimetry magnetic resonance imaging (MRI) of inhaled hyperpolarized 129Xe gas as a non-invasive reference to which airflow velocities calculated via CFD can be compared. To that end, we performed subject-specific CFD simulations in airway models derived from 1H MRI, and using respiratory flowrate measurements acquired synchronously with MRI. Airflow velocity vectors calculated by CFD simulations were then qualitatively and quantitatively compared to velocity maps derived from PC velocimetry MRI of inhaled hyperpolarized 129Xe gas. The results show both techniques produce similar spatial distributions of high velocity regions in the anterior-posterior and foot-head directions, indicating good qualitative agreement. Statistically significant correlations and low Bland-Altman bias between the local velocity values produced by the two techniques indicates quantitative agreement. This preliminary in vivo comparison of respiratory airway CFD and PC MRI of hyperpolarized 129Xe gas demonstrates the feasibility of PC MRI as a technique to validate respiratory CFD and forms the basis for further comprehensive validation studies. This study is therefore a first step in the pathway towards clinical adoption of respiratory CFD.

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