Upper airway reconstruction using long‐range optical coherence tomography: Effects of airway curvature on airflow resistance

Kimbell, Julia S.; Basu, Saikat; Garcia, Guilherme J. M.; Frank‐Ito, Dennis O.; Lazarow, Frances; Su, Erica; Protsenko, Dimitry; Chen, Zhongping; Rhee, John S.; Wong, Brian J. F.

doi:10.1002/lsm.23005

Cited by 29 publications

(24 citation statements)

References 49 publications

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“…Computer simulations of nasal airflow have many applications, such as quantifying the regional doses of nasal sprays [ 26 – 29 ] and virtual surgery planning for patients with nasal airway obstruction [ 6 , 7 , 9 – 12 , 30 , 31 ]. Many studies have evaluated how CFD-derived airflow variables are affected by numerical methods, including inlet boundary conditions [ 32 ], outlet boundary conditions [ 33 , 34 ], flow regime (laminar or turbulent) [ 24 ], and assumption of transient or steady-state flow [ 35 ]. One underlying assumption of CFD models that is rarely considered in the literature is the accuracy of the anatomic model [ 13 ].…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…Flow physics undergo a transition over this range; e.g. the 15 L/min flow through anatomically realistic nasal conduits lies in the laminar regime, [17][18][19][20][21][22][23][24][25][26][27][28] the flow structures however devolve into turbulence [29][30][31][32] at higher inhalation rates. Numerical schemes, that are used in this study to track respiratory transport, have been selected accordingly (see the supplementary methods section).…”

Section: /8mentioning

confidence: 99%

“…15 L/min) corresponds to comfortable resting breathing, with the viscous-laminar steady-state flow physics model standing in as a close approximation. [17][18][19][20][21][22][23][24][25][26][27][28] At higher flow rates (extreme values of which may sometimes lead to nasal valve collapse), the shear layer separation from the tortuous walls of the anatomic geometries results in turbulence. [29][30][31][32] While accounting for the turbulent characteristics of the ambient airflow, the study averages the droplet deposition percentages from implementation of two distinct categories of numerical schemes, viz.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Exposure to a COVID-19 carrier: transmission trends in respiratory tract and estimation of infectious dose

Basu

2020

Preprint

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How human respiratory physiology and inhaled airflow therein proceed to impact transmission of SARS-CoV-2, leading to the initial infection, is an open question. An answer can help determine the susceptibility of an individual on exposure to a COVID-2019 carrier and can also quantify the still-unknown infectious dose for the disease. Combining computational fluid mechanics-based tracking of respiratory transport in anatomic domains with sputum assessment data from hospitalized COVID-19 patients and earlier measurements of ejecta size distribution during regular speech - this study shows that the regional deposition of virus-laden inhaled droplets at the initial nasopharyngeal infection sites, located in the upper airway, peaks over the droplet size range of 2.5 - 19 microns; and reveals that the number of virions that can potentially establish the infection is, at most, of O(100).

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“…6 and 7, obstructions only take up a fraction of any given frame, with the rest consisting of reliable, unobstructed measurements. As shown in other aOCT studies, even with line-of-sight limitations, CFD simulations built from aOCT reconstructions can distinguish between diseased / healthy [1] and treated / untreated cohorts [41].…”

Section: Discussionmentioning

confidence: 71%

Geometric Validation of Continuous, Finely Sampled 3-D Reconstructions From aOCT and CT in Upper Airway Models

Price

Kimbell

et al. 2019

IEEE Trans. Med. Imaging

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Identification and treatment of obstructive airway disorders (OADs) are greatly aided by imaging of the geometry of the airway lumen. Anatomical optical coherence tomography (aOCT) is a promising high-speed and minimally invasive endoscopic imaging modality for providing micrometer-resolution scans of the upper airway. Resistance to airflow in OADs is directly caused by the reduction in luminal cross-sectional area (CSA). It is hypothesized that aOCT can produce airway CSA measurements as accurate as that from computed tomography (CT). Scans of machine hollowed cylindrical tubes were used to develop methods for segmentation and measurement of airway lumen in CT and aOCT. Simulated scans of virtual cones were used to validate 3-D resampling and reconstruction methods in aOCT. Then, measurements of two segments of a 3-D printed pediatric airway phantom from aOCT and CT independently were compared to ground truth CSA. In continuous unobstructed regions, the mean CSA difference for each phantom segment was 2.2 ± 3.5 and 1.5 ± 5.3 mm 2 for aOCT, and −3.4 ± 4.3 and −1.9 ± 1.2 mm 2 for CT. Because of the similar magnitude of these differences, these results support the hypotheses and underscore the potential for aOCT as a viable alternative to CT in airway imaging, while offering greater potential to capture respiratory dynamics.

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Upper airway reconstruction using long‐range optical coherence tomography: Effects of airway curvature on airflow resistance

Abstract: The results suggest that reconstruction of the upper airways from LR-OCT imaging data may not need to account for airway curvature to be predictive of surgical effects on airway resistance. Lasers Surg. Med. © 2018 Wiley Periodicals, Inc.

Cited by 29 publications

References 49 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

Exposure to a COVID-19 carrier: transmission trends in respiratory tract and estimation of infectious dose

Geometric Validation of Continuous, Finely Sampled 3-D Reconstructions From aOCT and CT in Upper Airway Models

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