Open Access
| Issue |
EPJ Web Conf.
Volume 358, 2026
EFM25 – Energy & Fluid Mechanics 2025
|
|
|---|---|---|
| Article Number | 01024 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/epjconf/202635801024 | |
| Published online | 12 March 2026 | |
- R. Mösges, Computational fluid dynamics of the nasal cavity, in Nasal Physiology and Pathophysiology of Nasal Disorders, (Springer-Verlag Berlin Heidelberg, pp. 247–255. 2013), DOI:10.1007/978-3-642-37250-6_19. [Google Scholar]
- K. Zhao, P. Malhotra, D. Rosen, P. Dalton, and E. A. Pribitkin, Computational Fluid Dynamics as Surgical Planning Tool: A Pilot Study on Middle Turbinate Resection. Anatomical Record, vol. 297, no. 11, pp. 2187–2195, (Nov. 2014), https://doi.org/10.1002/ar.23033 [Google Scholar]
- H. Salati, J. Bartley, and D. E. White, Computational Fluid Dynamics Simulation of Wall Shear Stress and Pressure Distribution from a Neti Pot During Nasal Saline Irrigation. J Med Biol Eng, vol. 41, no. 2, pp. 175–184, (Apr. 2021), DOI:10.1007/s40846-020-00589-6. [Google Scholar]
- M. Toğaçar, Z. Cömert, and B. Ergen, Classification of brain MRI using hyper column technique with convolutional neural network and feature selection method. Expert Syst Appl, vol. 149, p. 113274, Jul. 2020, https://doi.org/10.1016/j.eswa.2020.113274 [Google Scholar]
- G. G. Handsfield, C. H. Meyer, J. M. Hart, M. F. Abel, and S. S. Blemker, Relationships of 35 lower limb muscles to height and body mass quantified using MRI. J Biomech, vol. 47, no. 3, pp. 631–638, (Feb. 2014), https://doi.org/10.1016/j.jbiomech.2013.12.002. [Google Scholar]
- Y. Liu, M. R. Johnson, E. A. Matida, S. Kherani, and J. Marsan, Creation of a standardized geometry of the human nasal cavity. J Appl Physiol, vol. 106, p. 59, (2009), https://doi.org/10.1152/japplphysiol.90376.2008 [Google Scholar]
- D. Zwicker, K. Yang, S. Melchionna, M. P. Brenner, B. Liu, and R. W. Lindsay, Validated reconstructions of geometries of nasal cavities from CT scans. Biomed Phys Eng Express, vol. 4, no. 4, (Jun. 2018), DOI:10.1088/2057-1976/aac6af. [Google Scholar]
- F. Trnka, H. Schmirlerová, M. Májovský, D. Netuka, and M. Schmirler, Preparation of a Real Model of Nasal Cavities from Computed Tomography for Numerical Simulation. MATEC Web of Conferences, vol. 369, p. 01005, (2022), https://doi.org/10.1051/matecconf/202236901005 [CrossRef] [EDP Sciences] [Google Scholar]
- F. Trnka, H. Schmirlerová, M. Májovský, D. Netuka, and M. Schmirler, Examining the Impact of Inlet Shape on Nasal Cavity Flow Field: A Computational Investigation. MATEC Web of Conferences, vol. 383, p. 00021, (Oct. 2023), DOI: 10.1051/matecconf/202338300021. [Google Scholar]
- E. Seeram, Computed Tomography: Physical Principles and Recent Technical Advances. J Med Imaging Radiat Sci, vol. 41, no. 2, pp. 87–109, (Jun. 2010), https://doi.org/10.1016/j.jmir.2010.04.001 [Google Scholar]
- A. E. M. Eltorai, C. H. Hyman, and T. T. Healey, How Does Magnetic Resonance Imaging Work? In: Essential Radiology Review. (Springer, Cham. 2019). https://doi.org/10.1007/978-3-030-26044-6_163 [Google Scholar]
- Berger A. Magnetic resonance imaging. BMJ. 324(7328):35 (Jan 2002). DOI: 10.1136/bmj.324.7328.35 [Google Scholar]
- M. Raušová, The Effect of Neglecting Transient Flow in Nasal Cavity CFD Simulations, Master Thesis. CTU in Prague, Prague 2025 [Google Scholar]
- Trnka F, Schmirlerova H, Schmirler M, et al. Changes in Nasal Anatomy and Airflow After Endoscopic Resection of Pituitary Adenomas Using Computational Fluid Dynamics: A Pilot Study. Cureus (6): e85568, (June 08, 2025). DOI:10.7759/cureus.85568 [Google Scholar]
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