Abstract
Electrical impedance tomography (EIT) is a promising non-invasive, radiation-free imaging modality. Using EIT-derived index Center of ventilation (CoV), ventral-to-dorsal shifts in distribution of lung ventilation can be assessed. The methods of CoV calculation differ among authors and so does the segmentation of EIT images from which the CoV is calculated. The aim of this study is to compare the values of CoV obtained using different algorithms, applied in variously segmented EIT images. An animal trial (n=4) with anesthetized mechanically ventilated pigs was conducted. In one animal, acute respiratory distress syndrome (ARDS) was induced by repeated whole lung lavage. Incremental steps in positive end-expiratory pressure (PEEP), each with a value of 5 cmH2O (or 4 cmH2O in the ARDS model), were performed to reach total PEEP level of 25 cmH2O (or 22 cmH2O in the ARDS model). EIT data were acquired continuously during this PEEP trial. From each PEEP level, 30 tidal variation (TV) images were used for analysis. Functional regions of interest (ROI) were defined based on the standard deviation (SD) of pixel values, using threshold 15%–35% of maximum pixel SD. The results of this study show that there might be statistically significant differences between the values obtained using different methods for calculation of CoV. The differences occured in healthy animals as well as in the ARDS model. Both investigated algorithms are relatively insensitive to the image segmentation.
The original version of this chapter was inadvertently published with an incorrect chapter pagination 1258–1263 and DOI 10.1007/978-3-319-32703-7_241. The page range and the DOI has been re-assigned. The correct page range is 1264–1269 and the DOI is 10.1007/978-3-319-32703-7_242. The erratum to this chapter is available at DOI: 10.1007/978-3-319-32703-7_260
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-32703-7_260
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Frerichs I., Hinz J., Herrmann P., et al. Detection of local lung air content by electrical impedance tomography compared with electron beam CT J Appl Physiol. 2002;93:660-666.
Holder D.S.. Electrical Impedance Tomography: methods, history and applications. Philadelphia: Institute of Physics Pub. 2005.
Putensen C., Wrigge H., Zinserling J.. Electrical impedance tomography guided ventilation therapy Curr Opin Crit Care. 2007;13:344-350.
Adler A., Amato M.B., Arnold J.H., 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;33:679-694.
Frerichs I., Hahn G., Golisch W., Kurpitz M., Burchardi H., Hellige G. Monitoring perioperative changes in distribution of pulmonary ventilation by functional electrical impedance tomography Acta Anaesthesiol Scand. 1998;42:721-726.
Frerichs I., Dargaville P.A., Van Genderingen H., Morel D.R., Rimensberger P.C.. Lung volume recruitment after surfactant administration modifies spatial distribution of ventilation Am J Respir Crit Care Med. 2006;174:772-779.
Schibler A., Yuill M., Parsley C., Pham T., Gilshenan K., Dakin C.. Regional ventilation distribution in non-sedated spontaneously breathing newborns and adults is not different Pediatr Pulmonol. 2009;44:851-858.
Van Heerde M., Roubik K., Kopelent V., Kneyber M.C.J., Markhorst D.G.. Spontaneous breathing during high-frequency oscillatory ventilation improves regional lung characteristics in experimental lung injury Acta Anaesthesiol Scand. 2010;54:1248-1256.
Radke O.C., Schneider T., Heller A.R., Koch T.. Spontaneous breathing during general anesthesia prevents the ventral redistribution of ventilation as detected by electrical impedance tomography: A randomized trial Anesthesiology. 2012;116:1227-1234.
Blankman P., Hasan D., Erik G.J., Gommers D.. Detection of ‘best’ positive end-expiratory pressure derived from electrical impedance tomography parameters during a decremental positive end-expiratory pressure trial Crit Care. 2014;18.
Zhao Z., Frerichs I., Pulletz S., Müller-Lisse U., Möller K.. The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation Physiol Meas. 2014;35:1083-1093.
Schaefer M.S., Wania V., Bastin B., et al. Electrical impedance tomography during major open upper abdominal surgery: A pilot-study BMC Anesthesiol. 2014;14.
Luepschen H., Meier T., Grossherr M., Leibecke T., Karsten J., Leonhardt S.. Protective ventilation using electrical impedance tomography Physiol Meas. 2007;28:S247-S260.
Pulletz S., Van Genderingen H.R., Schmitz G., et al. Comparison of different methods to define regions of interest for evaluation of regional lung ventilation by EIT Physiol Meas. 2006;27:S115.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sobota, V., Roubik, K. (2016). Center of Ventilation—Methods of Calculation Using Electrical Impedance Tomography and the Influence of Image Segmentation. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_242
Download citation
DOI: https://doi.org/10.1007/978-3-319-32703-7_242
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32701-3
Online ISBN: 978-3-319-32703-7
eBook Packages: EngineeringEngineering (R0)