Effects of CT resolution and radiodensity threshold on the CFD evaluation of nasal airflow

Quadrio, Maurizio; Pipolo, Carlotta; Corti, Stefano; Messina, Francesco; Pesci, Chiara; Saibene, Alberto Maria; Zampini, Samuele; Felisati, Giovanni

doi:10.1007/s11517-015-1325-4

Cited by 31 publications

(28 citation statements)

References 32 publications

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“…One critical step that has received little attention in the CFD literature is the segmentation of the nasal cavity and paranasal sinuses from medical images [ 13 ]. Segmentation is the process by which 3-dimensional (3D) models of the airspace are created from magnetic resonance images (MRI) or computed tomography (CT) scans [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of nasal airflow variables computed via computational fluid dynamics to the computed tomography segmentation threshold

et al. 2018

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Computational fluid dynamics (CFD) allows quantitative assessment of transport phenomena in the human nasal cavity, including heat exchange, moisture transport, odorant uptake in the olfactory cleft, and regional delivery of pharmaceutical aerosols. The first step when applying CFD to investigate nasal airflow is to create a 3-dimensional reconstruction of the nasal anatomy from computed tomography (CT) scans or magnetic resonance images (MRI). However, a method to identify the exact location of the air-tissue boundary from CT scans or MRI is currently lacking. This introduces some uncertainty in the nasal cavity geometry. The radiodensity threshold for segmentation of the nasal airways has received little attention in the CFD literature. The goal of this study is to quantify how uncertainty in the segmentation threshold impacts CFD simulations of transport phenomena in the human nasal cavity. Three patients with nasal airway obstruction were included in the analysis. Pre-surgery CT scans were obtained after mucosal decongestion with oxymetazoline. For each patient, the nasal anatomy was reconstructed using three different thresholds in Hounsfield units (-800HU, -550HU, and -300HU). Our results demonstrate that some CFD variables (pressure drop, flowrate, airflow resistance) and anatomic variables (airspace cross-sectional area and volume) are strongly dependent on the segmentation threshold, while other CFD variables (intranasal flow distribution, surface area) are less sensitive to the segmentation threshold. These findings suggest that identification of an optimal threshold for segmentation of the nasal airway from CT scans will be important for good agreement between in vivo measurements and patient-specific CFD simulations of transport phenomena in the nasal cavity, particularly for processes sensitive to the transnasal pressure drop. We recommend that future CFD studies should always report the segmentation threshold used to reconstruct the nasal anatomy.

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Section: Introductionmentioning

confidence: 99%

Sensitivity of nasal airflow variables computed via computational fluid dynamics to the computed tomography segmentation threshold

et al. 2018

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“…A HU-value of -218 was selected in order to acquire an anatomically coherent 3D reconstruction of the air-filled cavities. More details on CT, threshold value choice and 3D reconstruction, obtained via the software 3D Slicer, can be found in a previous publication by our group 22 . Nasal thermal water inhalation therapies consist of inhaling vapour droplets through the nose while the mouth re-mains closed.…”

Section: Methodsmentioning

confidence: 99%

“…The procedure is based on an entirely open-source software toolchain, whose main elements are 3D Slicer 21 for 3D reconstruction, OpenFOAM (OpenCFD Limited, ESI Group Company, Bracknell, UK) for numerical simulations and Paraview (Kitware, New York, US) for visualisation of the results. Their use for nasal flow simulations has been described in a previous work from our group 22 . Both inhalation and aerosol therapies are considered in an effort to improve the current understanding of nasal flow and droplet deposition, with a focus on possible clinical implications.…”

Section: Introductionmentioning

confidence: 99%

Thermal water delivery in the nose: experimental results describing droplet deposition through computational fluid dynamics

Buijs

Covello

Pipolo

et al. 2019

Acta Otorhinolaryngol Ital

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Thermal water therapies have a role in treating various inflammatory disorders dating back to ancient Greece. Several studies have demonstrated beneficial effects of thermal water inhalations for upper respiratory disorders, such as improvement of mucociliary function and reduction of inflammatory cell infiltration. This experimental study describes the numerical investigation and clinical implications of thermal water droplet deposition in the nasal cavity of a single patient. To our knowledge, the numerical flow simulations described are the first investigations specifically designed for thermal water applications. To simulate nasal airflow, a patient-specific 3D computer model was created from a CT scan. The numerical approach is based on the Large Eddy Simulation (LES) technique and builds entirely upon open-source software. Deposition on mucosa was studied for two droplet sizes (5 and 10 µm diameter), corresponding to common thermal therapy applications (aerosol and vapour inhalation). The simulations consider steady inspiration at two different (low and moderate) breathing intensities. The results of this preliminary study show specific deposition patterns that favour droplet deposition in the middle meatus region to the inferior meatus, with particle size-and breathing intensity-related effects. These global data on particle deposition differ from findings related to the single-phase nasal airflow, which is more evenly distributed between the middle and inferior meatus. The potential clinical consequences of deposition data are discussed. The study furthermore provides evidence for the effectiveness of thermal aerosol and vapour inhalation therapies in reaching important areas of nasal mucosa with considerable clinical significance.

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“…Image voxels with a Hounsfield Unit (HU) above −400 were masked as tissue. Thus, only voxels below this threshold were considered as candidates for the segmentation of the nasal cavity 23,24 .…”

Section: Image Segmentationmentioning

confidence: 99%

Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity

Brüning

Hildebrandt

Heppt

et al. 2020

Sci Rep

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This study's objective was the generation of a standardized geometry of the healthy nasal cavity. An average geometry of the healthy nasal cavity was generated using a statistical shape model based on 25 symptom-free subjects. Airflow within the average geometry and these geometries was calculated using fluid simulations. Integral measures of the nasal resistance, wall shear stresses (WSS) and velocities were calculated as well as cross-sectional areas (CSA). Furthermore, individual WSS and static pressure distributions were mapped onto the average geometry. The average geometry featured an overall more regular shape that resulted in less resistance, reduced WSS and velocities compared to the median of the 25 geometries. Spatial distributions of WSS and pressure of the average geometry agreed well compared to the average distributions of all individual geometries. The minimal CSA of the average geometry was larger than the median of all individual geometries (83.4 vs. 74.7 mm²). The airflow observed within the average geometry of the healthy nasal cavity did not equal the average airflow of the individual geometries. While differences observed for integral measures were notable, the calculated values for the average geometry lay within the distributions of the individual parameters. Spatially resolved parameters differed less prominently.The nose is the main passageway for respired air to flow from ambient to the lungs and vice versa. Air flowing through the nose is humidified, tempered and cleansed from particles, which could harm the intricate structures of the lungs. While the anatomy of the nose can easily be investigated from the outside using either a speculum or an endoscope, the nature of healthy nasal airflow is not yet fully understood. Nonetheless, there were extensive efforts as well as remarkable progress to better understand the complex airflow within the nose.While investigation of the airflow within the nasal cavity began with in-vitro experiments using either cadaver castings or upscaled models 1-3 , numerical simulation of nasal airflow became the quasi standard within the last decade and is now widely used 4-6 . Here, the patient-specific geometry of the nasal cavity is reconstructed using computed tomography (CT) scans.Recently, first studies were able to reveal correlations between numerically calculated flow parameters and the perceived nasal patency of a patient. Zhao et al. were able to show, that a significant correlation between cooling of the mucosal layer, a process that is associated with trigeminal function, is correlated with patency ratings of the nose 7,8 . In more recent studies, they were able to reveal differences in wall shear stress and heat flux between symptomatic and asymptomatic patients with septal perforations 9 and they were also able to show, that numerical simulations might help to understand complex relationships between surgical procedures and the development of empty nose syndrome 10 . Sanmiguel-Rojas et al. proposed another approach, where two non-dimensional par...

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Effects of CT resolution and radiodensity threshold on the CFD evaluation of nasal airflow

Cited by 31 publications

References 32 publications

Sensitivity of nasal airflow variables computed via computational fluid dynamics to the computed tomography segmentation threshold

Sensitivity of nasal airflow variables computed via computational fluid dynamics to the computed tomography segmentation threshold

Thermal water delivery in the nose: experimental results describing droplet deposition through computational fluid dynamics

Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity

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