Airflow in Tracheobronchial Tree of Subjects with Tracheal Bronchus Simulated Using CT Image Based Models and CFD Method

  • Shouliang Qi
  • Baihua Zhang
  • Yong Yue
  • Jing Shen
  • Yueyang Teng
  • Wei Qian
  • Jianlin Wu
Image & Signal Processing
  • 87 Downloads
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

Tracheal Bronchus (TB) is a rare congenital anomaly characterized by the presence of an abnormal bronchus originating from the trachea or main bronchi and directed toward the upper lobe. The airflow pattern in tracheobronchial trees of TB subjects is critical, but has not been systemically studied. This study proposes to simulate the airflow using CT image based models and the computational fluid dynamics (CFD) method. Six TB subjects and three health controls (HC) are included. After the geometric model of tracheobronchial tree is extracted from CT images, the spatial distribution of velocity, wall pressure, wall shear stress (WSS) is obtained through CFD simulation, and the lobar distribution of air, flow pattern and global pressure drop are investigated. Compared with HC subjects, the main bronchus angle of TB subjects and the variation of volume are large, while the cross-sectional growth rate is small. High airflow velocity, wall pressure, and WSS are observed locally at the tracheal bronchus, but the global patterns of these measures are still similar to those of HC. The ratio of airflow into the tracheal bronchus accounts for 6.6–15.6% of the inhaled airflow, decreasing the ratio to the right upper lobe from 15.7–21.4% (HC) to 4.9–13.6%. The air into tracheal bronchus originates from the right dorsal near-wall region of the trachea. Tracheal bronchus does not change the global pressure drop which is dependent on multiple variables. Though the tracheobronchial trees of TB subjects present individualized features, several commonalities on the structural and airflow characteristics can be revealed. The observed local alternations might provide new insight into the reason of recurrent local infections, cough and acute respiratory distress related to TB.

Keywords

Tracheal bronchus The tracheobronchial tree Computational fluid dynamics Airflow CT scan 

Abbreviations

BC

Boundary conditions

COPD

Chronic obstructive pulmonary disease

CFD

Computational fluid dynamics

CT

Computed Tomography

LLL

Left lower lobe

LUL

Left upper lobe

LA

Lobar distribution

PFT

Pulmonary function tests

RLL

Right lower lobe

RML

Right middle lobe

RUL

Right upper lobe

STL

Stereo lithography

TB

Tracheal bronchus

Notes

Acknowledgments

The authors also acknowledge Mr. Paul Young for his helpful language editing.

Authors’ Contributions

SQ: proposed the idea, performed experiments, analyzed the data, made discussions and composed the manuscript together with BZ, YT. YY and JS: provided CT images and radiology instruction, and made the discussions. WQ and JW: directed the experiments and made discussions. All authors read and approved the final manuscript.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Berrocal, T., Madrid, C., Novo, S., et al., Congenital anomalies of the tracheobronchial tree, lung, and mediastinum: Embryology, radiology, and pathology. Radiographics A Review Publication of the Radiological Society of North America Inc. 24(1):e17, 2004.CrossRefGoogle Scholar
  2. 2.
    Chassagnon, G., Morel, B., and Carpentier, E., Tracheobronchial branching abnormalities: Lobe-based classification scheme. Radiographics A Review Publication of the Radiological Society of North America Inc. 36(4):150115, 2016.CrossRefGoogle Scholar
  3. 3.
    Ming, Z., and Lin, Z., Evaluation of tracheal bronchus in Chinese children using multidetector CT. Pediatr. Radiol. 37:1230–1234, 2007.CrossRefPubMedGoogle Scholar
  4. 4.
    Desir, A., and Ghaye, B., Congenital abnormalities of intrathoracic airways. Radiol. Clin. N. Am. 47(2):203–225, 2009.CrossRefPubMedGoogle Scholar
  5. 5.
    Laroia, A.T., Thompson, B.H., Laroia, S.T., and van Beek, E.J.R., Modern imaging of the tracheo-bronchial tree. World. J. Radiol. 2(7):237–248, 2010.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Pu, J., Gu, S., Liu, S., Zhu, S., Wilson, D., Siegfried, J.M., and Gur, D., CT based computerized identification and analysis of human airways: A review. Med. Phys. 39(5):2603–2616, 2011.CrossRefGoogle Scholar
  7. 7.
    Rubin, B.K., Dhand, R., Ruppel, G.L., Branson, R.D., and Hess, D.R., Respiratory care year inreview2010: Part 1. Asthma, COPD, pulmonary function testing, ventilator-associated pneumonia. Respir. Care. 56(4):488–502, 2011.CrossRefPubMedGoogle Scholar
  8. 8.
    Ruppel, G.L., and Enright, P.L., Pulmonary function testing. Respir. Care. 57(1):165–175, 2012.CrossRefPubMedGoogle Scholar
  9. 9.
    Burrowes, K.S., De Backer, J., Smallwood, R., Sterk, P.J., Gut, I., Wirix-Speetjens, R., Siddiqui, S., Owers-Bradley, J., Wild, J., Maier, D., and Brightling, C., Multi-scale computational models of the airways to unravel the pathophysiological mechanisms in asthma and chronic obstructive pulmonary disease (AirPROM). Interface Focus. 3(2):20120057, 2013.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kleinstreuer, C., and Zhang, Z., Airflow and particle transport in the human respiratory system. Annu. Rev. Fluid. Mech. 42(1–4):301–334, 2010.CrossRefGoogle Scholar
  11. 11.
    Yang, X.L., Liu, Y., and Luo, H.Y., Respiratory flow in obstructed airways. J. Biomech. 39(15):2743, 2006.CrossRefPubMedGoogle Scholar
  12. 12.
    Luo, H.Y., Liu, Y., and Yang, X.L., Particle deposition in obstructed airways. J. Biomech. 40(14):3096, 2007.CrossRefPubMedGoogle Scholar
  13. 13.
    Sul, B., Wallqvist, A., Morris, M.J., et al., A computational study of the respiratory airflow characteristics in normal and obstructed human airways. Comput. Biol. Med. 52(3):130–143, 2014.CrossRefPubMedGoogle Scholar
  14. 14.
    Lin, C.L., Tawhai, M.H., Mclennan, G., and Hoffman, E.A., Multiscale simulation of gas flow in subject-specific models of the human lung. IEEE Eng. Med. Biol. 28(3):25–33, 2009.CrossRefGoogle Scholar
  15. 15.
    De Backer, J.W., Vos, W.G., Gorlé, C.D., et al., Flow analyses in the lower airways: Patient-specific model and boundary conditions. Med. Eng. Phys. 30(7):872, 2008.CrossRefPubMedGoogle Scholar
  16. 16.
    De Rochefort, L., Vial, L., Fodil, R., Maitre, X., Louis, B., Isabey, D., Caillibotte, G., Thiriet, M., Bittoun, J., Durand, E., and Sbirlea-Apiou, G., In vitro validation of computational fluid dynamic simulation in human proximal airways with hyperpolarized 3He magnetic resonance phase-contrast velocimetry. J. Appl. Physiol. 102(5):2012–2023, 2007.CrossRefPubMedGoogle Scholar
  17. 17.
    De Backer JW, Vos WG, Vinchurkar SC, Claes R, Drollmann A, Wulfrank D, Parizel PM, Germonpre P, De Backer W (2010) Validation of computational fluid dynamics in CT-based airway models with SPECT/CT. Radiol 257(3):854–862.Google Scholar
  18. 18.
    Vial, L., Perchet, D., Fodil, R., Caillibotte, G., Fetita, C., Preteux, F., Beigelman-Aubry, C., Grenier, P., Thiriet, M., Isabey, D., and Sbirlea-Apiou, G., Airflow modeling of steady inspiration in two realistic proximal airway trees reconstructed from human thoracic tomodensitometric images. Comput. Methods. Biomech. Biomed. Engin. 8(4):267–277, 2005.CrossRefPubMedGoogle Scholar
  19. 19.
    Qi, S., Li, Z., Yue, Y., van Triest, H.J.W., and Kang, Y., Computational fluid dynamics simulation of airflow in the trachea and main bronchi for the subjects with left pulmonary artery sling. Biomed. Eng. Online. 13:85, 2014.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Qi, S., Li, Z., Yue, Y., van Triest, H.J.W., Kang, Y., and Qian, W., Simulation analysis of deformation and stress of tracheal and main bronchial wall for the subjects with left pulmonary artery sling. J. Mech. Med. Biol. 15(6):1540053, 2015.CrossRefGoogle Scholar
  21. 21.
    Qi, S., Zhang, B., Teng, Y., Li, J., Yue, Y., Kang, Y., and Qian, W., Transient dynamics simulation of airflow in a CT-scanned human airway tree: More or fewer terminal bronchi? Comput. Math. Methods Med. 2017:1969023, 2017.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    De Backer, J.W., Vos, W.G., Devolder, A., Verhulst, S.L., Germonpre, P., Wuyts, F.L., Parizel, P.M., and De Backer, W., Computational fluid dynamics can detect changes in airway resistance in asthmatics after acute bronchodilation. J. Biomech. 41(1):106–113, 2008.CrossRefPubMedGoogle Scholar
  23. 23.
    Ho, C.Y., Liao, H.M., Tu, C.Y., Huang, C.Y., Shih, C.M., Su, M.Y., Chen, J.H., and Shih, T.C., Numerical analysis of airflow alteration in central airways following tracheobronchial stent placement. Exp. Hematol. Oncol. 1(1):23, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Chen, F.L., Horng, T.L., and Shih, T.C., Simulation analysis of airflow alteration in the trachea following the vascular ring surgery based on CT images using the computational fluid dynamics method. J. X-ray Sci. Technol. 22(2):213–225, 2014.Google Scholar
  25. 25.
    Bos, A.C., van Holsbeke, C., de Backer, J.W., van Westreenen, M., Janssens, H.M., Vos, W.G., et al., Patient-specific modeling of regional antibiotic concentration levels in airways of patients with cystic fibrosis: Are we dosing high enough? PLoS ONE10. 3:e0118454, 2015.CrossRefGoogle Scholar
  26. 26.
    Luo, H.Y., and Liu, Y., Modeling the bifurcating flow in a CT-scanned human lung airway. J. Biomech. 41(12):2681–2688, 2008.CrossRefPubMedGoogle Scholar
  27. 27.
    Mylavarapu, G., Murugappan, S., Mihaescu, M., et al., Validation of computational fluid dynamics methodology used for human upper airway flow simulations. J. Biomech. 42(10):1553–1559, 2009.CrossRefPubMedGoogle Scholar
  28. 28.
    Gemci, T., Ponyavin, V., Chen, Y., et al., Computational model of airflow in upper 17 generations of human respiratory tract. J. Biomech. 41(9):2047–2054, 2008.CrossRefPubMedGoogle Scholar
  29. 29.
    Walters, D.K., Burgreen, G.W., Lavallee, D.M., Thompson, D.S., and Hester, R.L., Efficient, physiologically realistic lung airflow simulations. IEEE Trans. Biomed. Eng. 58(10):3016–3019, 2011.CrossRefPubMedGoogle Scholar
  30. 30.
    Ma, B., and Lutchen, K.R., An anatomically based hybrid computational model of the human lung and its application to low frequency oscillatory mechanics. Ann. Biomed. Eng. 34(11):1691–1704, 2006.CrossRefPubMedGoogle Scholar
  31. 31.
    Chen, S.J., Lee, W.J., Wang, J.K., et al., Usefulness of three-dimensional electron beam computed tomography for evaluating tracheobronchial anomalies in children with congenital heart disease. Am. J. Cardiol. 92(4):483–486, 2003.CrossRefPubMedGoogle Scholar
  32. 32.
    Balásházy, I., and Hofmann, W., Deposition of aerosols in asymmetric airway bifurcations. J. Aerosol Sci. 26(2):273–292, 1995.CrossRefGoogle Scholar
  33. 33.
    Middleton, R.M., Littleton, J.T., Brickey, D.A., et al., Obstructed tracheal bronchus as a cause of post-obstructive pneumonia. J. Thorac. Imaging. 10(3):223–224, 1995.CrossRefPubMedGoogle Scholar
  34. 34.
    Doolittle, A.M., and Mair, E.A., Tracheal bronchus: Classification, endoscopic analysis, and airway management. Otolaryngology--head and neck surgery: Official journal of American Academy of otolaryngology-head and neck Surgery. 126(3):240–243, 2002.CrossRefGoogle Scholar
  35. 35.
    Panigada, S., Sacco, O., Girosi, D., et al., Recurrent severe lower respiratory tract infections in a child with abnormal tracheal morphology. Pediatr. Pulmonol. 44(2):192–194, 2009.CrossRefPubMedGoogle Scholar
  36. 36.
    Seo, T., Ando, H., Kaneko, K., et al., Two cases of prenatally diagnosed congenital lobar emphysema caused by lobar bronchial atresia. J. Pediatr. Surg. 41(11):17–20, 2006.CrossRefGoogle Scholar
  37. 37.
    Morikawa, N., Kuroda, T., Honna, T., et al., Congenital bronchial atresia in infants and children. J. Pediatr. Surg. 40(12):1822–1826, 2005.CrossRefPubMedGoogle Scholar
  38. 38.
    Freitas, R.K., and Schroder, W., Numerical investigation of the three-dimensional flow in a human lung model. J. Biomech. 41(11):2446–2457, 2008.CrossRefPubMedGoogle Scholar
  39. 39.
    Mott, L.S., Park, J., Gangell, C.L., de Klerk, N.H., Sly, P.D., Murray, C.P., and Stick, S.M., Distribution of early structural lung changes due to cystic fibrosis detected with chest computed tomography. J. Pediatr. 163(1):243–248, 2013.CrossRefPubMedGoogle Scholar
  40. 40.
    Nakano, Y., Van Tho, N., Yamada, H., Osawa, M., and Nagao, T., Radiological approach to asthma and COPD--the role of computed tomography. Allergol. Int. 58(3):323–331, 2009.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityShenyangChina
  2. 2.Key Laboratory of Medical Image Computing of Northeastern University (Ministry of Education)ShenyangChina
  3. 3.Department of RadiologyShengjing Hospital of China Medical UniversityShenyangChina
  4. 4.Department of RadiologyAffiliated Zhongshan Hospital of Dalian UniversityDalianChina
  5. 5.College of Engineering, University of Texas at El PasoEl PasoUSA

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