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

Abstract Purpose Active anterior rhinomanometry (AAR) and computed tomography (CT) are standardized methods for the evaluation of nasal obstruction. Recent attempts to correlate AAR with CT-based computational fluid dynamics (CFD) have been controversial. We aimed to investigate this correlation and agreement based on an in-house developed procedure. Methods In a pilot study, we retrospectively examined five subjects scheduled for septoplasty, along with preoperative digital volume tomography and AAR. The simulation was performed with Sailfish CFD, a lattice Boltzmann code. We examined the correlation and agreement of pressure derived from AAR (RhinoPress) and simulation (SimPress) and these of resistance during inspiration by 150 Pa pressure drop derived from AAR (RhinoRes150) and simulation (SimRes150). For investigation of correlation between pressures and between resistances, a univariate analysis of variance and a Pearson’s correlation were performed, respectively. For investigation of agreement, the Bland–Altman method was used. Results The correlation coefficient between RhinoPress and SimPress was r = 0.93 (p < 0.001). RhinoPress was similar to SimPress in the less obstructed nasal side and two times greater than SimPress in the more obstructed nasal side. A moderate correlation was found between RhinoRes150 and SimRes150 (r = 0.65; p = 0.041). Conclusion The simulation of rhinomanometry pressure by CT-based CFD seems more feasible with the lattice Boltzmann code in the less obstructed nasal side. In the more obstructed nasal side, error rates of up to 100% were encountered. Our results imply that the pressure and resistance derived from CT-based CFD and AAR were similar, yet not same.

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Inferior meatus augmentation procedure (IMAP) normalizes nasal airflow patterns in empty nose syndrome patients via computational fluid dynamics (CFD) modeling

International Forum of Allergy & Rhinology ◽

10.1002/alr.22720 ◽

2020 ◽

Author(s):

Jennifer Malik ◽

Sachi Dholakia ◽

Barak M. Spector ◽

Angela Yang ◽

Dayoung Kim ◽

...

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Cfd Modeling ◽

Nasal Airflow ◽

Inferior Meatus ◽

Computational Fluid Dynamics Cfd ◽

Empty Nose Syndrome ◽

Augmentation Procedure

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Computational Fluid Dynamics Applied to Cardiac Computed Tomography for Noninvasive Quantification of Fractional Flow Reserve

Journal of the American College of Cardiology ◽

10.1016/j.jacc.2012.11.083 ◽

2013 ◽

Vol 61 (22) ◽

pp. 2233-2241 ◽

Cited By ~ 547

Author(s):

Charles A. Taylor ◽

Timothy A. Fonte ◽

James K. Min

Keyword(s):

Fluid Dynamics ◽

Computed Tomography ◽

Computational Fluid Dynamics ◽

Fractional Flow Reserve ◽

Cardiac Computed Tomography ◽

Fractional Flow ◽

Flow Reserve

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An investigation of computational fluid dynamics of human nasal airflow using LS-DYNA®

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/884/1/012111 ◽

2020 ◽

Vol 884 ◽

pp. 012111

Author(s):

V N Riazuddin ◽

C C Chen ◽

Ahmad Faridzul ◽

M A Jasni ◽

J Chen

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Nasal Airflow

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Computed tomography angiography as an adjunct to computational fluid dynamics for prediction of oxygenator thrombus formation

Perfusion ◽

10.1177/0267659120944105 ◽

2020 ◽

pp. 026765912094410

Author(s):

Robert G Conway ◽

Jiafeng Zhang ◽

Jean Jeudy ◽

Charles Evans ◽

Tieluo Li ◽

...

Keyword(s):

Fluid Dynamics ◽

Computed Tomography ◽

Computational Fluid Dynamics ◽

Computed Tomography Angiography ◽

Thrombus Formation ◽

Flow Analysis ◽

Flow Characteristics ◽

Region Of Interest ◽

Computational Fluid Dynamics Analysis ◽

Clot Burden

Introduction: Extracorporeal membrane oxygenation circuit performance can be compromised by oxygenator thrombosis. Stagnant blood flow in the oxygenator can increase the risk of thrombus formation. To minimize thrombogenic potential, computational fluid dynamics is frequently applied for identification of stagnant flow conditions. We investigate the use of computed tomography angiography to identify flow patterns associated with thrombus formation. Methods: A computed tomography angiography was performed on a Quadrox D oxygenator, and video densitometric parameters associated with flow stagnation were measured from the acquired videos. Computational fluid dynamics analysis of the same oxygenator was performed to establish computational fluid dynamics–based flow characteristics. Forty-one Quadrox D oxygenators were sectioned following completion of clinical use. Section images were analyzed with software to determine oxygenator clot burden. Linear regression was used to correlate clot burden to computed tomography angiography and computational fluid dynamics–based flow characteristics. Results: Clot burden from the explanted oxygenators demonstrated a well-defined pattern, with the largest clot burden at the corner opposite the blood inlet and outlet. The regression model predicted clot burden by region of interest as a function of time to first opacification on computed tomography angiography (R2 = 0.55). The explanted oxygenator clot burden map agreed well with the computed tomography angiography predicted clot burden map. The computational fluid dynamics parameter of residence time, when summed in the Z-direction, was partially predictive of clot burden (R2 = 0.35). Conclusion: In the studied oxygenator, clot burden follows a pattern consistent with clinical observations. Computed tomography angiography–based flow analysis provides a useful adjunct to computational fluid dynamics–based flow analysis in understanding oxygenator thrombus formation.

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