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Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
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Lung area estimation using functional tidal electrical impedance variation images and active contouring.

Silke Borgmann1,2, Kim Linz1,2, Christian Braun1,2

  • 1Department of Anesthesiology and Critical Care, Medical Center, University of Freiburg, Freiburg, Germany.

Physiological Measurement
|June 28, 2022
PubMed
Summary

Accurate lung area estimation is vital for electrical impedance tomography (EIT) to monitor lung mechanics. New methods using functional tidal images and neural networks improve lung and heart area estimation in EIT.

Keywords:
active contouringconvolutional neural networkelectrical impedance tomographyfunctional tidal imageslung area estimation

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Area of Science:

  • Medical Imaging
  • Physiological Monitoring
  • Computational Biology

Background:

  • Electrical impedance tomography (EIT) is essential for assessing lung mechanics.
  • Accurate lung area estimation is critical for interpreting EIT data.
  • Current methods may lack precision in complex thoracic imaging.

Purpose of the Study:

  • To develop and evaluate novel methods for precise lung area estimation in EIT.
  • To assess the impact of heart region visibility and lung area mirroring on estimation accuracy.
  • To integrate lung and heart area estimation into a routine for EIT analysis.

Main Methods:

  • Two novel methods were developed: one using functional tidal images and another employing active contouring.
  • A convolutional neural network was trained to detect the heart region in tidal images.
  • Performance was evaluated using impedance magnitudes and standard deviations, with analysis of lung area mirroring effects.

Main Results:

  • The functional tidal image method yielded the most accurate lung area estimates.
  • Lung area mirroring significantly affected the accuracy of both estimation methods.
  • The neural network achieved 94% accuracy in classifying heart visibility; a heart template subtraction was effective when the heart was not visible.

Conclusions:

  • A robust routine for estimating both lung and heart areas in EIT images was successfully developed.
  • The functional tidal image approach offers superior lung area estimation.
  • Accurate heart area delineation is achievable and improves overall EIT analysis.