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European Archives of Oto-Rhino-Laryngology

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Open Access 01-05-2021 | Computed Tomography | Rhinology

Three-dimensional modeling and automatic analysis of the human nasal cavity and paranasal sinuses using the computational fluid dynamics method

Authors: Dmitry Tretiakow, Krzysztof Tesch, Jarosław Meyer-Szary, Karolina Markiet, Andrzej Skorek

Published in: European Archives of Oto-Rhino-Laryngology | Issue 5/2021

Abstract

Purpose

The goal of this study was to develop a complete workflow allowing for conducting computational fluid dynamics (CFD) simulation of airflow through the upper airways based on computed tomography (CT) and cone-beam computed tomography (CBCT) studies of individual adult patients.

Methods

This study is based on CT images of 16 patients. Image processing and model generation of the human nasal cavity and paranasal sinuses were performed using open-source and freeware software. 3-D Slicer was used primarily for segmentation and new surface model generation. Further processing was done using Autodesk^® Meshmixer TM. The governing equations are discretized by means of the finite volume method. Subsequently, the corresponding algebraic equation systems were solved by OpenFOAM software.

Results

We described the protocol for the preparation of a 3-D model of the nasal cavity and paranasal sinuses and highlighted several problems that the future researcher may encounter. The CFD results were presented based on examples of 3-D models of the patient 1 (norm) and patient 2 (pathological changes).

Conclusion

The short training time for new user without a prior experience in image segmentation and 3-D mesh editing is an important advantage of this type of research. Both CBCT and CT are useful for model building. However, CBCT may have limitations. The Q criterion in CFD illustrates the considerable complication of the nasal flow and allows for direct evaluation and quantitative comparison of various flows and can be used for the assessment of nasal airflow.

Tretiakow D, Skorek A (2019) Nasal septum turbinate and its significance for a rhinosurgeon. Pol Przegląd Otorynolaryngologiczny 8(4):1–5. https://doi.org/10.5604/01.3001.0013.5462CrossRef

Kim SK, Heo GE, Seo A, Na Y, Chung S-K (2014) Correlation between nasal airflow characteristics and clinical relevance of nasal septal deviation to nasal airway obstruction. Respir Physiol Neurobiol 192:95–101. https://doi.org/10.1016/j.resp.2013.12.010CrossRefPubMed

Kucybała I, Janik KA, Ciuk S, Storman D, Urbanik A (2017) Nasal septal deviation and concha bullosa—do they have an impact on maxillary sinus volumes and prevalence of maxillary sinusitis? Pol J Radiol 82:126–133. https://doi.org/10.12659/PJR.900634CrossRefPubMedPubMedCentral

Tomblinson CM, Cheng MR, Lal D, Hoxworth JM (2016) The impact of middle turbinate concha bullosa on the severity of inferior turbinate hypertrophy in patients with a deviated nasal septum. Am J Neuroradiol 37(7):1324–1330. https://doi.org/10.3174/ajnr.A4705CrossRefPubMed

Tretiakow D, Skorek A (2020) Scientific tools for collecting and analysing medical data in rhinology. Pol Przegląd Otorynolaryngologiczny 9(1):1–5. https://doi.org/10.5604/01.3001.0013.9128CrossRef

Teixeira J, Certal V, Chang ET, Camacho M (2016) Nasal septal deviations: a systematic review of classification systems. Plast Surg Int 2016:1–8. https://doi.org/10.1155/2016/7089123CrossRef

Aziz T, Biron VL, Ansari K, Flores-Mir C (2014) Measurement tools for the diagnosis of nasal septal deviation: a systematic review. J Otolaryngol Head Neck Surg 43(1):11. https://doi.org/10.1186/1916-0216-43-11CrossRefPubMedPubMedCentral

Marro A, Bandukwala T, Mak W (2016) Three-dimensional printing and medical imaging: a review of the methods and applications. Curr Probl Diagn Radiol 45(1):2–9. https://doi.org/10.1067/j.cpradiol.2015.07.009CrossRefPubMed

Hinton TJ, Jallerat Q, Palchesko RN, Park JH, Grodzicki MS, Shue H-J et al (2015) Three-dimensional printing of complex biological structures by freeform reversible embedding of suspended hydrogels. Sci Adv. 1(9):e1500758. https://doi.org/10.1126/sciadv.1500758CrossRefPubMedPubMedCentral

10.

Çakli H, Cingi C, Ay Y, Oghan F, Ozer T, Kaya E (2012) Use of cone beam computed tomography in otolaryngologic treatments. Eur Arch Oto-Rhino-Laryngol 269(3):711–720. https://doi.org/10.1007/s00405-011-1781-xCrossRef

11.

Gregurić T, Trkulja V, Baudoin T, Grgić MV, Šmigovec I, Kalogjera L (2017) Association between computed tomography findings and clinical symptoms in chronic rhinosinusitis with and without nasal polyps. Eur Arch Oto-Rhino-Laryngol 274(5):2165–2173. https://doi.org/10.1007/s00405-016-4446-yCrossRef

12.

Derakhshanfar S, Mbeleck R, Xu K, Zhang X, Zhong W, Xing M (2018) 3D bioprinting for biomedical devices and tissue engineering: a review of recent trends and advances. Bioact Mater 3(2):144–156. https://doi.org/10.1016/j.bioactmat.2017.11.008CrossRefPubMedPubMedCentral

13.

Yan Q, Dong H, Su J, Han J, Song B, Wei Q et al (2018) A review of 3D printing technology for medical applications. Engineering 4(5):729–742. https://doi.org/10.1016/j.eng.2018.07.021CrossRef

14.

Luo H, Meyer-Szary J, Wang Z, Sabiniewicz R, Liu Y (2017) Three-dimensional printing in cardiology: current applications and future challenges. Cardiol J. 24(4):436–444. https://doi.org/10.5603/CJ.a2017.0056CrossRefPubMed

15.

Eslahpazir M, Krull R, Krühne U (2019) Computational fluid dynamics. Comprehensive biotechnology. Elsevier, Oxford, pp 95–107. https://doi.org/10.1016/B978-0-444-64046-8.00123-3CrossRef

16.

Moreddu E, Meister L, Philip-Alliez C, Triglia J-M, Medale M, Nicollas R (2019) Computational fluid dynamics in the assessment of nasal obstruction in children. Eur Ann Otorhinolaryngol Head Neck Dis 136(2):87–92. https://doi.org/10.1016/j.anorl.2018.11.008CrossRefPubMed

17.

Moreddu E, Meister L, Dabadie A, Triglia J-M, Médale M, Nicollas R (2020) Numerical simulation of nasal airflows and thermal air modification in newborns. Med Biol Eng Comput 58(2):307–317. https://doi.org/10.1007/s11517-019-02092-wCrossRefPubMed

18.

Farnoud A, Baumann I, Rashidi MM, Schmid O, Gutheil E (2020) Simulation of patient-specific bi-directional pulsating nasal aerosol dispersion and deposition with clockwise 45° and 90° nosepieces. Comput Biol Med 123:103816. https://doi.org/10.1016/j.compbiomed.2020.103816CrossRefPubMed

19.

Farnoud A, Cui X, Baumann I, Gutheil E (2017) Numerical simulation of the dispersion and deposition of a spray carried by a pulsating airflow in a patient-specific human nasal cavity. At Sprays 27(11):913–928. https://doi.org/10.1615/AtomizSpr.2017020782CrossRef

20.

Covello V, Pipolo C, Saibene A, Felisati G, Quadrio M (2018) Numerical simulation of thermal water delivery in the human nasal cavity. Comput Biol Med 100:62–73. https://doi.org/10.1016/j.compbiomed.2018.06.029CrossRefPubMed

21.

Wilcox DC (2006) Turbulence modeling for CFD, 3rd edn. DCW Industries Inc, La Canada

22.

Langtry RB, Menter FR (2009) Correlation-Based Transition Modeling for Unstructured parallelized computational fluid dynamics codes. AIAA J 47(12):2894–2906. https://doi.org/10.2514/1.42362CrossRef

23.

Menter FR, Langtry R, Völker S (2006) Transition modelling for general purpose CFD codes. Flow Turbul Combust 77(1–4):277–303. https://doi.org/10.1007/s10494-006-9047-1CrossRef

24.

Menter FR (1994) Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J 32(8):1598–1605. https://doi.org/10.2514/3.12149CrossRef

25.

OpenFOAM Foundation (2014) OpenFOAM—the open source CFD toolbox—user guide. OpenFOAM Foundation

26.

Issa R (1986) Solution of the implicitly discretised fluid flow equations by operator-splitting. J Comput Phys 62(1):40–65. https://doi.org/10.1016/0021-9991(86)90099-9CrossRef

27.

Balakin BV, Farbu E, Kosinski P (2017) Aerodynamic evaluation of the empty nose syndrome by means of computational fluid dynamics. Comput Methods Biomech Biomed Eng 20(14):1554–1561. https://doi.org/10.1080/10255842.2017.1385779CrossRef

28.

Maza G, Li C, Krebs JP, Otto BA, Farag AA, Carrau RL et al (2019) Computational fluid dynamics after endoscopic endonasal skull base surgery-possible empty nose syndrome in the context of middle turbinate resection. Int Forum Allergy Rhinol 9(2):204–211. https://doi.org/10.1002/alr.22236CrossRefPubMed

29.

Patel RG, Garcia GJM, Frank-Ito DO, Kimbell JS, Rhee JS (2015) Simulating the nasal cycle with computational fluid dynamics. Otolaryngol Neck Surg 152(2):353–360. https://doi.org/10.1177/0194599814559385CrossRef

30.

Gaberino C, Rhee JS, Garcia GJM (2017) Estimates of nasal airflow at the nasal cycle mid-point improve the correlation between objective and subjective measures of nasal patency. Respir Physiol Neurobiol 238:23–32. https://doi.org/10.1016/j.resp.2017.01.004CrossRefPubMedPubMedCentral

31.

Burgos MA, Sanmiguel-Rojas E, del Pino C, Sevilla-García MA, Esteban-Ortega F (2017) New CFD tools to evaluate nasal airflow. Eur Arch Oto-Rhino-Laryngol 274(8):3121–3128. https://doi.org/10.1007/s00405-017-4611-yCrossRef

32.

Orth RC, Wallace MJ, Kuo MD (2009) C-arm Cone-beam CT: general principles and technical considerations for use in interventional radiology. J Vasc Interv Radiol 20(7):S538–S544. https://doi.org/10.1016/j.jvir.2009.04.026CrossRefPubMed

33.

Valtonen O, Ormiskangas J, Kivekäs I, Rantanen V, Dean M, Poe D et al (2020) Three-Dimensional printing of the nasal cavities for clinical experiments. Sci Rep 10(1):502. https://doi.org/10.1038/s41598-020-57537-2CrossRefPubMedPubMedCentral

34.

Ormiskangas J, Olli V, Kivekäs I, Dean M, Poe D, Järnstedt J et al (2020) Assessment of PIV performance in validating CFD models from nasal cavity CBCT scans. Respir Physiol Neurobiol. https://doi.org/10.1016/j.resp.2020.103508CrossRefPubMed

35.

Lee KB, Jeon YS, Chung S-K, Kim SK (2016) Effects of partial middle turbinectomy with varying resection volume and location on nasal functions and airflow characteristics by CFD. Comput Biol Med 77:214–221. https://doi.org/10.1016/j.compbiomed.2016.08.014CrossRefPubMed

36.

Hunt JCR, Wray AA, Moin P (1988) Eddies, streams, and convergence zones in turbulent flows. Cent Turbul Res Proc Summer Progr S88:193–208

37.

Haller G (2005) An objective definition of a vortex. J Fluid Mech 525:1–26. https://doi.org/10.1017/S0022112004002526CrossRef

38.

Tesch K (2013) On invariants of fluid mechanics tensors. Task Q 17(3–4):1000–1008

39.

Tesch K, Kludzinska K, Doerffer P (2015) Investigation of the aerodynamics of an innovative vertical-axis wind turbine. Flow Turbul Combust 95(4):739–754. https://doi.org/10.1007/s10494-015-9615-3CrossRef

Title: Three-dimensional modeling and automatic analysis of the human nasal cavity and paranasal sinuses using the computational fluid dynamics method
Authors: Dmitry Tretiakow
Krzysztof Tesch
Jarosław Meyer-Szary
Karolina Markiet
Andrzej Skorek
Publication date: 01-05-2021
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Published in: European Archives of Oto-Rhino-Laryngology / Issue 5/2021
Print ISSN: 0937-4477
Electronic ISSN: 1434-4726
DOI: https://doi.org/10.1007/s00405-020-06428-3

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Springer Medicine

Three-dimensional modeling and automatic analysis of the human nasal cavity and paranasal sinuses using the computational fluid dynamics method

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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