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Three-dimensional pulmonary monitoring using focused electrical impedance measurements Cover

Three-dimensional pulmonary monitoring using focused electrical impedance measurements

Journal of Electrical Bioimpedance
Volume 9 (2018): Issue 1 (January 2018)
By: Jakob Orschulik,  Diana Pokee,  Tobias Menden,  Steffen Leonhardt and  Marian Walter  
Open Access
|Dec 2018

Figures & Tables

Fig. 1

Sample sensitivity distributions and their impact on the criteria Selectivity Sel and Homogeneity Hom. a.) shows the perfect sensitivity distribution with infinitely high Selectivity and Homogeneity values. b.) has a high Selectivity, but a low Homogeneity as areas close to the edge of the ROI have no sensitivity. In contrast, c.) has a high Homogeneity, but also a low Selectivity as lots of sensitivity is present in regions outside the ROI. Note, that the local impedance contribution can be determined by multiplying the sensitivity with the reciprocal local conductivity. Figure adapted from [7].
Sample sensitivity distributions and their impact on the criteria Selectivity Sel and Homogeneity Hom. a.) shows the perfect sensitivity distribution with infinitely high Selectivity and Homogeneity values. b.) has a high Selectivity, but a low Homogeneity as areas close to the edge of the ROI have no sensitivity. In contrast, c.) has a high Homogeneity, but also a low Selectivity as lots of sensitivity is present in regions outside the ROI. Note, that the local impedance contribution can be determined by multiplying the sensitivity with the reciprocal local conductivity. Figure adapted from [7].

Fig. 2

Finite element model used in this study (model from [14]). The lungs were split into eight regions of interest. For each ROI, a specific tetrapolar electrode configuration was placed at the body surface. In this paper, the results for the regions of interest left, back, bottom and right, front, top region will be presented. The respective electrode configurations are based on the results in [6] and shown from different views in the two rows. The focused ROI is highlighted in yellow.
Finite element model used in this study (model from [14]). The lungs were split into eight regions of interest. For each ROI, a specific tetrapolar electrode configuration was placed at the body surface. In this paper, the results for the regions of interest left, back, bottom and right, front, top region will be presented. The respective electrode configurations are based on the results in [6] and shown from different views in the two rows. The focused ROI is highlighted in yellow.

Fig. 3

Breathing cycle for the simulation of ventilation. The conductivity of the lungs was simulated on 21 levels between fully deflated and fully inflated.
Breathing cycle for the simulation of ventilation. The conductivity of the lungs was simulated on 21 levels between fully deflated and fully inflated.

Fig. 4

Water tank phantom used for experimental validation. Four lung lobes are represented by balloons. All lungs can be opened and closed separately by an individual valve. The ventilation is realized by a mechanical ventilator (MV). The fourth valve is not visible as it is behind the other valves. In this image, the measurement setup for the EIT reference measurement using a single electrode belt is visualized.
Water tank phantom used for experimental validation. Four lung lobes are represented by balloons. All lungs can be opened and closed separately by an individual valve. The ventilation is realized by a mechanical ventilator (MV). The fourth valve is not visible as it is behind the other valves. In this image, the measurement setup for the EIT reference measurement using a single electrode belt is visualized.

Fig. 5

Simulation results for an impedance measurement focused on the left, back, bottom region of the lung.
Simulation results for an impedance measurement focused on the left, back, bottom region of the lung.

Fig. 6

Simulation results for an impedance measurement focused on the right, front, top region of the lung.
Simulation results for an impedance measurement focused on the right, front, top region of the lung.

Fig. 7

Reconstructed EIT images based on simulated pathologies. In the top row, the healthy reference and pathological examples for the left, back, bottom region of interest are presented. In the bottom row, the same healthy reference and pathology for the right, front, top region of interest is shown. The respective region is highlighted in yellow in the left and right view. It can be seen that in the lbb case, a high inhomogeneity can be observed whereas in the rft case, almost no impact on the EIT image is visible.
Reconstructed EIT images based on simulated pathologies. In the top row, the healthy reference and pathological examples for the left, back, bottom region of interest are presented. In the bottom row, the same healthy reference and pathology for the right, front, top region of interest is shown. The respective region is highlighted in yellow in the left and right view. It can be seen that in the lbb case, a high inhomogeneity can be observed whereas in the rft case, almost no impact on the EIT image is visible.

Fig. 8

Result of the experimental validation at the water phantom in five phases (I – V). At each phase, the balloons, which represent specific lung lobes, were disabled in the respective region as highlighted in the second row. The corresponding EIT image is shown for each phase. The images show a difference to the baseline measurement. However, no information on the vertical location of the disabled region can be extracted. Below the images, the filtered raw data of two focused electrode configurations (left bottom and right top) is shown. The location of the disabled balloons can be clearly seen in the respective focused impedance data.
Result of the experimental validation at the water phantom in five phases (I – V). At each phase, the balloons, which represent specific lung lobes, were disabled in the respective region as highlighted in the second row. The corresponding EIT image is shown for each phase. The images show a difference to the baseline measurement. However, no information on the vertical location of the disabled region can be extracted. Below the images, the filtered raw data of two focused electrode configurations (left bottom and right top) is shown. The location of the disabled balloons can be clearly seen in the respective focused impedance data.

Numerical results of the evaluation for the regions left, back, bottom (lbb) and right, front, top (rft)_ The respective electrode positions are shown in Fig_ 2

RegionSelHomI%Iabs
lbb31.30.562.94%0.11%
rft9.151.187.44%0.07%
DOI: https://doi.org/10.2478/joeb-2018-0013 | Journal eISSN: 1891-5469
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Language: English
Page range: 84 - 95
Submitted on: Dec 17, 2018
Published on: Dec 31, 2018
Published by: University of Oslo
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Electrical Impedance Analysis,
Pulmonary,
Monitoring,
Regional Analysis
Related subjects:
Engineering,
Bioengineering and biomedical engineering,
Biomedical electronics,
Life sciences,
Biophysics,
Medicine,
Biomedical engineering,
Physics,
Spectroscopy and metrology

© 2018 Jakob Orschulik, Diana Pokee, Tobias Menden, Steffen Leonhardt, Marian Walter, published by University of Oslo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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