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Journal of Clinical Monitoring and Computing

, Volume 32, Issue 4, pp 753–761 | Cite as

Ventilation inhomogeneity in obstructive lung diseases measured by electrical impedance tomography: a simulation study

  • B. SchullckeEmail author
  • S. Krueger-Ziolek
  • B. Gong
  • R. A. Jörres
  • U. Mueller-Lisse
  • K. Moeller
Original Research
  • 163 Downloads

Abstract

Electrical impedance tomography (EIT) has mostly been used in the Intensive Care Unit (ICU) to monitor ventilation distribution but is also promising for the diagnosis in spontaneously breathing patients with obstructive lung diseases. Beside tomographic images, several numerical measures have been proposed to quantitatively assess the lung state. In this study two common measures, the ‘Global Inhomogeneity Index’ and the ‘Coefficient of Variation’ were compared regarding their capability to reflect the severity of lung obstruction. A three-dimensional simulation model was used to simulate obstructed lungs, whereby images were reconstructed on a two-dimensional domain. Simulations revealed that minor obstructions are not adequately recognized in the reconstructed images and that obstruction above and below the electrode plane may result in misleading values of inhomogeneity measures. EIT measurements on several electrode planes are necessary to apply these measures in patients with obstructive lung diseases in a promising manner.

Keywords

Electrical impedance tomography Ventilation inhomogeneity Obstructive lung diseases Simulation study 

Notes

Acknowledgements

This work has been partially supported by the Federal Ministry of Education and Research (BMBF) under Grant No. 03FH038I3 (MOSES).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain any studies with human participant performed by any of the authors.

References

  1. 1.
    Gong B, et al. Electrical impedance tomography: functional lung imaging on its way to clinical practice? Expert review of respiratory medicine. 2015;9(6):721–37.CrossRefPubMedGoogle Scholar
  2. 2.
    Frerichs I. Electrical impedance tomography (EIT) in applications related to lung and ventilation: a review of experimental and clinical activities. Physiol Meas. 2000;21(2):R1-21.CrossRefPubMedGoogle Scholar
  3. 3.
    Pikkemaat R, et al. Electrical impedance tomography: New diagnostic possibilities using regional time constant maps. Appl Cardiopul P (ACP). 2012;16:212–25.Google Scholar
  4. 4.
    Krueger-Ziolek S, et al. Multi-layer ventilation inhomogeneity in cystic fibrosis. Respir Physiol Neurobiol. 2016;233:25–32.CrossRefPubMedGoogle Scholar
  5. 5.
    Zhao Z, et al. Regional ventilation in cystic fibrosis measured by electrical impedance tomography. J Cyst Fibros. 2012;11(5):412–8.CrossRefPubMedGoogle Scholar
  6. 6.
    Zhao Z, et al. Regional airway obstruction in cystic fibrosis determined by electrical impedance tomography in comparison with high resolution CT. Physiol Meas. 2013;34(11):N107-14.CrossRefPubMedGoogle Scholar
  7. 7.
    Vogt B, et al. Spatial and temporal heterogeneity of regional lung ventilation determined by electrical impedance tomography during pulmonary function testing. J Appl Physiol. 2012;113(7):1154–61.CrossRefPubMedGoogle Scholar
  8. 8.
    Frerichs I, et al. Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the Translational EIT development study group. Thorax 2017;72:83–93.Google Scholar
  9. 9.
    Zhao Z, et al. Evaluation of an electrical impedance tomography-based global inhomogeneity index for pulmonary ventilation distribution. Intensive Care Med. 2009;35(11):1900–6.CrossRefPubMedGoogle Scholar
  10. 10.
    Zhao Z, et al. The EIT-based global inhomogeneity index is highly correlated with regional lung opening in patients with acute respiratory distress syndrome. BMC Res Notes. 2014;7:82.Google Scholar
  11. 11.
    Becher T, et al. Functional regions of interest in electrical impedance tomography: a secondary analysis of two clinical studies. PLoS ONE. 2016;11(3):e0152267.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Becher T, et al. Influence of tidal volume on ventilation inhomogeneity assessed by electrical impedance tomography during controlled mechanical ventilation. Physiol Meas. 2015;36(6):1137.CrossRefPubMedGoogle Scholar
  13. 13.
    Adler A, Lionheart WR. Uses and abuses of EIDORS: an extensible software base for EIT. Physiol Meas. 2006;27(5):S25-42.CrossRefPubMedGoogle Scholar
  14. 14.
    Schöberl J. NETGEN an advancing front 2D/3D-mesh generator based on abstract rules. Comput Visual Sci. 1997;1(1):41–52.CrossRefGoogle Scholar
  15. 15.
    Cheng K-S, et al. Electrode models for electric current computed tomography. Biomed Eng IEEE Trans. 1989;36(9):918–24.CrossRefGoogle Scholar
  16. 16.
    Krueger-Ziolek S, et al. Positioning of electrode plane systematically influences EIT imaging. Physiol Meas. 2015;36(6):1109–18.CrossRefPubMedGoogle Scholar
  17. 17.
    Adler A, Guardo R, Berthiaume Y. Impedance imaging of lung ventilation: do we need to account for chest expansion? IEEE Trans Biomed Eng. 1996;43(4):414–20.CrossRefPubMedGoogle Scholar
  18. 18.
    Soleimani M. Computational aspects of low frequency electrical and electromagnetic tomography: a review study. Int J Numer Anal Model. 2008;5(3):407–40.Google Scholar
  19. 19.
    Polydorides N, Lionheart WR. A Matlab toolkit for three-dimensional electrical impedance tomography: a contribution to the Electrical Impedance and Diffuse Optical Reconstruction Software project. Meas Sci Technol. 2002;13(12):1871.CrossRefGoogle Scholar
  20. 20.
    Graham BM, Adler A. Objective selection of hyperparameter for EIT. Physiol Meas. 2006;27(5):S65-79.CrossRefPubMedGoogle Scholar
  21. 21.
    Putensen C, Zinserling J, Wrigge H. Electrical impedance tomography for monitoring of regional ventilation in critically III patients. In: Vincent JL, editor. Intensive care medicine. New York, NY: Springer; 2006.Google Scholar
  22. 22.
    Adler A, et al. Simple FEMs aren’t as good as we thought: experiences developing EIDORS v3. 3. Proc. Conf. EIT (Hannover, NH, USA), 2008.Google Scholar
  23. 23.
    Adler A, Guardo R. Electrical impedance tomography: regularized imaging and contrast detection. IEEE Trans Med Imaging. 1996;15(2):170–9.CrossRefPubMedGoogle Scholar
  24. 24.
    Grychtol B, Müller B, Adler A. 3D EIT image reconstruction with GREIT. Physiol Meas. 2016;37(6):785.CrossRefPubMedGoogle Scholar
  25. 25.
    Adler A, et al. Whither lung EIT: where are we, where do we want to go and what do we need to get there? Physiol Meas. 2012;33(5):679–94.CrossRefPubMedGoogle Scholar
  26. 26.
    Bayford RH. Bioimpedance tomography (electrical impedance tomography). Annu Rev Biomed Eng. 2006;8:63–91.CrossRefPubMedGoogle Scholar
  27. 27.
    Borsic A, et al. In vivo impedance imaging with total variation regularization. IEEE Trans Med Imaging. 2010;29(1):44–54.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Institute of Technical MedicineFurtwangen UniversityVS-SchwenningenGermany
  2. 2.Department of RadiologyLudwig-Maximilians-UniversitätMunichGermany
  3. 3.Institute and Outpatient Clinic for Occupational, Social and Environmental MedicineLudwig-Maximilians-UniversitätMunichGermany

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