An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography

Huang, R.K.; Nedanoski, Anthony; Fletcher, David F.; Singh, Narinder; Schmid, Jérôme; Young, Paul M.; Stow, Nicholas W.; Bi, Lei; Traini, Daniela; Wong, Eugene; Phillips, Craig L.; Grunstein, Ronald R.; Kim, Jinman

doi:10.1016/j.compbiomed.2019.103505

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…Computational Fluid Dynamics (CFD) is a useful platform to study airflow in the nasal cavity. Recently, it has been used as a non-invasive method to investigate flow behaviour [9][10][11][12][13][14][15][16][17][18] . Li et al 19 compared results using various turbulence models against the experimental work of Hahn, Scherer, and Mozell 4 replicating the unilateral cavity geometry with the nasopharynx omitted.…”

Section: Introductionmentioning

confidence: 99%

Pressure distribution and flow dynamics in a nasal airway using a scale resolving simulation

et al. 2021

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Airflow through the nasal cavity exhibits a wide variety of fluid dynamic behaviors due to the intricacy of the nasal geometry. The flow is naturally unsteady and perhaps turbulent, despite Computational Fluid Dynamics (CFD) in the literature being assumed as having a steady laminar flow. Time-dependent simulations can be used to generate detailed data with the potential to uncover new flow behavior, although they are more computationally intensive than steady-state simulations. Furthermore, verification of CFD results has relied on a reported pressure drop (e.g., nasal resistance) across the nasal airway although the geometries used are different. This study investigated the unsteady nature of inhalation at flow rates of 10 l/min, 15 l/min, 20 l/min, and 30 l/min. A scale resolving CFD simulation using a hybrid Reynolds-averaged Navier–Stokes--large eddy simulation model was used and compared with experimental measurements of the pressure distribution and the overall pressure drop in the nasal cavity. The experimental results indicated a large pressure drop across the nasal valve and across the nasopharynx, with the latter attributed to a narrow cross-sectional area. At a flowrate of 30 l/min, the CFD simulations showed that the anterior half of the nasal cavity displayed dominantly laminar but disturbed flow behavior in the form of velocity fluctuations. The posterior half of the nasal cavity displayed turbulent activity, characterized by erratic fluctuating velocities, which was enhanced by the wider cross-sectional areas in the coronal plane. At 15 l/min, the flow field was laminar dominant with very little disturbance, confirming a steady-state laminar flow assumption is viable at this flow rate.

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Section: Introductionmentioning

confidence: 99%

Pressure distribution and flow dynamics in a nasal airway using a scale resolving simulation

et al. 2021

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“…7 million cells in less than 6 hours on a personal computer with 4 cores [20]. Huang et al [21] introduce a segmentation framework that is optimized to reduce the number of manual steps needed to conduct numerical simulations. Segmentation is performed using a statistical shape modeling method.…”

Section: Automatizing Simulations Of Respiratory Flowsmentioning

confidence: 99%

A machine-learning-based method for automatizing lattice-Boltzmann simulations of respiratory flows

et al. 2022

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Many simulation workflows require to prepare the data for the simulation manually. This is time consuming and leads to a massive bottleneck when a large number of numerical simulations is requested. This bottleneck can be overcome by an automated data processing pipeline. Such a novel pipeline is developed for a medical use case from rhinology, where computer tomography recordings are used as input and flow simulation data define the results. Convolutional neural networks are applied to segment the upper airways and to detect and prepare the in- and outflow regions for accurate boundary condition prescription in the simulation. The automated process is tested on three cases which have not been used to train the networks. The accuracy of the pipeline is evaluated by comparing the network-generated output surfaces to those obtained from a semi-automated procedure performed by a medical professional. Except for minor deviations at interfaces between ethmoidal sinuses, the network-generated surface is sufficiently accurate. To further analyze the accuracy of the automated pipeline, flow simulations are conducted with a thermal lattice-Boltzmann method for both cases on a high-performace computing system. The comparison of the results of the respiratory flow simulations yield averaged errors of less than 1% for the pressure loss between the in- and outlets, and for the outlet temperature. Thus, the pipeline is shown to work accurately and the geometrical deviations at the ethmoidal sinuses to be negligible.

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“…These are mainly based on segmentation procedures to produce an anatomically precise model derived from CT and magnetic resonance imaging (Figure 10). [40][41][42] One critical step is the segmentation of the nasal cavities from imaging data sets. The anatomical complexity of these structures and the extensive connection to other airway components, such as the ethmoid sinuses and the pharynx, makes the segmentation of the only nasal cavity difficult without the manual intervention of an experienced operator.…”

Section: Segmentation Analysismentioning

confidence: 99%

“…45 Huang et al elaborated on an automatic segmentation in order to obtain nasal models from CT for the analysis of CFD, showing high segmentation accuracy and an average distance error of 0.3 mm. 40…”

Section: Segmentation Analysismentioning

confidence: 99%

Nasal cavities and the nasal septum: Anatomical variants and assessment of features with computed tomography

et al. 2020

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The nasal cavities are complex anatomical structures with high inter-individual variability that relates to different functions. Different anatomic variants may manifest at this site, mainly belonging to the nasal septum and turbinates. Precise knowledge of the anatomy and variants is fundamental for both radiologists and ENT surgeons. This article provides an overview of the main anatomic variants and their frequency, according to the existing literature, as well as ongoing research on nasal cavity segmentation in order to obtain personal 3D models and to predict post-surgical results.

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An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography

Cited by 11 publications

References 34 publications

Pressure distribution and flow dynamics in a nasal airway using a scale resolving simulation

Pressure distribution and flow dynamics in a nasal airway using a scale resolving simulation

A machine-learning-based method for automatizing lattice-Boltzmann simulations of respiratory flows

Nasal cavities and the nasal septum: Anatomical variants and assessment of features with computed tomography

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