Advertisement

Computational Modeling for Simulating Obstructive Lung Diseases Based on Geometry Processing Methods

  • Stavros NousiasEmail author
  • Aris S. Lalos
  • Konstantinos Moustakas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9745)

Abstract

Obstructive lung diseases (e.g. asthma & COPD) are life-long inflammatory diseases that result in narrowing of the lung airways, reducing the airflow that reaches the oxygen exchanging areas. The knowledge and understanding of the pathophysiology behind these diseases could lead to improved diagnosis and assessment, through the development of computational models that take into account details related to lung geometry, lung mechanical features and the dynamics of the fluid motion inside the lungs. Although there are several works that considers some of the aforementioned characteristics, none of them provides a fully integrated solution. To overcome this limitation, we have developed a software tool that combines geometry processing methods with 3D computational fluid dynamics (CFD), allowing us to simulate in a realistic way the effects of bronchoconstriction in the dynamics of the air flow in the airways of the lung. The developed tool is expected to provide useful information regarding the way that either drug or other harmful particles are being dispersed inside the lungs during different levels of inflammation.

Keywords

Computational models Computational fluid dynamics Geometry processing 

Notes

Acknowledgments

This work has been funded by the H2020-PHC-2014-2015 Project MyAirCoach (Grant Agreement No. 643607).

References

  1. 1.
    World health organization: World Health Statistics Geneva, Switzerland (2008)Google Scholar
  2. 2.
    Au, O.K.C., Tai, C.L., Chu, H.K., Cohen-Or, D., Lee, T.Y.: Skeleton extraction by mesh contraction. In: ACM Transactions on Graphics (TOG), vol. 27, p. 44. ACM (2008)Google Scholar
  3. 3.
    Darquenne, C., van Ertbruggen, C., Prisk, G.K.: Convective flow dominates aerosol delivery to the lung segments. J. Appl. Physiol. 111(1), 48–54 (2011)CrossRefGoogle Scholar
  4. 4.
    De Backer, J., Vos, W., Gorle, C., Germonpré, P., Partoens, B., Wuyts, F., Parizel, P.M., De Backer, W.: Flow analyses in the lower airways: patient-specific model and boundary conditions. Med. Eng. Phys. 30(7), 872–879 (2008)CrossRefGoogle Scholar
  5. 5.
    Ferziger, J.H., Peric, M.: Computational Methods for Fluid Dynamics. Springer, Heidelberg (2012)zbMATHGoogle Scholar
  6. 6.
    Jasak, H., Jemcov, A., Tukovic, Z.: Openfoam: A C++ library for complex physics simulations. In: International Workshop on Coupled Methods in Numerical Dynamics, vol. 1000, pp. 1–20 (2007)Google Scholar
  7. 7.
    Lambert, A.R., O’shaughnessy, P.T., Tawhai, M.H., Hoffman, E.A., Lin, C.L.: Regional deposition of particles in an image-based airway model: large-eddy simulation and left-right lung ventilation asymmetry. Aerosol Sci. Technol. 45(1), 11–25 (2011)CrossRefGoogle Scholar
  8. 8.
    Lambert, R.K., Wilson, T.A., Hyatt, R.E., Rodarte, J.R.: A computational model for expiratory flow. J. Appl. Physiol. 52(1), 44–56 (1982)Google Scholar
  9. 9.
    Lin, C.L., Tawhai, M.H., Hoffman, E.A.: Multiscale image-based modeling and simulation of gas flow and particle transport in the human lungs. Wiley Interdisc. Rev. Syst. Biol. Med. 5(5), 643–655 (2013)CrossRefGoogle Scholar
  10. 10.
    Rausch, S., Martin, C., Bornemann, P., Uhlig, S., Wall, W.: Material model of lung parenchyma based on living precision-cut lung slice testing. J. Mech. Behav. Biomed. Mater. 4(4), 583–592 (2011)CrossRefGoogle Scholar
  11. 11.
    Schmidt, A., Zidowitz, S., Kriete, A., Denhard, T., Krass, S., Peitgen, H.O.: A digital reference model of the human bronchial tree. Comput. Med. Imaging Graph. 28(4), 203–211 (2004)CrossRefGoogle Scholar
  12. 12.
    Swan, A.J., Clark, A.R., Tawhai, M.H.: A computational model of the topographic distribution of ventilation in healthy human lungs. J. Theor. Biol. 300, 222–231 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Tawhai, M.H., Nash, M.P., Lin, C.L., Hoffman, E.A.: Supine and prone differences in regional lung density and pleural pressure gradients in the human lung with constant shape. J. Appl. Physiol. 107(3), 912–920 (2009)CrossRefGoogle Scholar
  14. 14.
    Yin, Y., Choi, J., Hoffman, E.A., Tawhai, M.H., Lin, C.L.: A multiscale MDCT image-based breathing lung model with time-varying regional ventilation. J. Comput. Phys. 244, 168–192 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Stavros Nousias
    • 1
    Email author
  • Aris S. Lalos
    • 1
  • Konstantinos Moustakas
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of PatrasPatrasGreece

Personalised recommendations