Accuracy of virtual rhinomanometry

Karbowski, Krzysztof; Kopiczak, Bartosz; Chrzan, Robert; Gawlik, Jolanta; Szaleniec, Joanna

doi:10.2478/pjmpe-2023-0008

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…

m 2 /s and

m 2 /s are the mass diffusion coefficient of the boundary layer and mucous membrane [ 14 ]. The thickness of the boundary layer and the mucous membrane layer thickness is

mm and

mm, respectively [ 34 ].…”

Section: Numerical Simulation Methodsmentioning

confidence: 99%

“…Numerical analysis has been used in various studies such as airflow diagnostics in patients with sleep apnoea, deposition fraction, and drug delivery efficiency [ 12 , 13 ]. Recent research has confirmed that the use of numerical modelling to analyse airflow through the nasal cavity (virtual rhinomanometry) can be considered a reliable method [ 14 ]. Many studies have shown that simulations and numerical modelling, particularly numerical fluid dynamics (CFD), are mature and reliable enough to be treated as part of personalized medicine.…”

Section: Introductionmentioning

confidence: 99%

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Physical Conditions Prevailing in the Nasal and Maxillary Sinus Cavities Based on Numerical Simulation

et al. 2023

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Background and Objectives: This paper presents a unique study that links the physical conditions in the nasal passage with conditions that favour the development of bacterial strains and the colonization of the mucous membranes of the nose and paranasal sinuses. The physical parameters considered were air flow, pressure, humidity, and temperature. Materials and Methods: Numerical models of the human nose and maxillary sinus were retrospectively reconstructed from CT images of generally healthy young subjects. The state-of-the-art numerical methods and tools were then used to determine the temperature, humidity, airflow velocity, and pressure at specific anatomical locations. Results: The results were compared with optimal conditions for bacterial growth in the nose and sinuses. Conclusions: Temperature, humidity, air velocity, and pressure were shown to play critical roles in the selection and distribution of microorganisms. Furthermore, certain combinations of physical parameters can favour mucosal colonisation by various strains of bacteria.

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“…

m 2 /s and

m 2 /s are the mass diffusion coefficient of the boundary layer and mucous membrane [ 14 ]. The thickness of the boundary layer and the mucous membrane layer thickness is

mm and

mm, respectively [ 34 ].…”

Section: Numerical Simulation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Physical Conditions Prevailing in the Nasal and Maxillary Sinus Cavities Based on Numerical Simulation

et al. 2023

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Application of CFD airflows to aid in the diagnosis of nasal obstructions

Kopiczak,

Karbowski,

Nering

et al. 2024

J. Phys.: Conf. Ser.

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This paper describes the results of experimental and numerical studies of airflow through the nasal cavities of human. The flow geometry obtained from the CT scan is used to produce a 3D printout for the experimental measurements and the numerical models used in the CFD analyses. The effect of the segmentation level on the numerical results is presented. The segmentation, described in the article, aims to find air-filled areas. Additionally, the experimental verification of the numerical flow results is conducted.

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Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Johnsen

2024

Bioengineering

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Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in, e.g., the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. This paper consists of three distinct parts—an introduction to and review of the field of computational rhinology, a review of the published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of this paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

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Accuracy of virtual rhinomanometry

Cited by 3 publications

References 38 publications

Physical Conditions Prevailing in the Nasal and Maxillary Sinus Cavities Based on Numerical Simulation

Physical Conditions Prevailing in the Nasal and Maxillary Sinus Cavities Based on Numerical Simulation

Application of CFD airflows to aid in the diagnosis of nasal obstructions

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

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