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Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
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Automated robust test framework for electrical impedance tomography.

Pascal O Gaggero1, Andy Adler, Andreas D Waldmann

  • 1Institute for Human Centered Engineering, Bern University of Applied Sciences, Biel/Bienne, Switzerland.

Physiological Measurement
|May 27, 2015
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Summary
This summary is machine-generated.

An automated system was developed for systematic testing of electrical impedance tomography (EIT) devices. This system ensures reliable performance evaluation and aids in optimizing EIT device parameters for better image quality.

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Area of Science:

  • Biomedical Engineering
  • Electrical Engineering
  • Medical Imaging

Background:

  • Electrical Impedance Tomography (EIT) is an emerging medical imaging technique.
  • Standardized testing procedures are crucial for EIT device development and validation.
  • Current methods for assessing EIT performance lack systematic and automated approaches.

Purpose of the Study:

  • To propose an automated test system and procedure for systematic evaluation of EIT devices.
  • To enable reliable, repeatable, and accurate calculation of EIT system performance metrics.
  • To facilitate comparison and optimization of EIT devices and their operating parameters.

Main Methods:

  • Development of an automated test system utilizing a saline phantom and an industrial robot arm.
  • Implementation of a systematic procedure for EIT device testing.
  • Evaluation of image quality and contrast detection across various stimulation and measurement patterns.

Main Results:

  • The automated system provides reliable and repeatable performance figures of merit for EIT devices.
  • Optimal EIT imaging results were achieved when current injection electrodes were spaced between 45 and 170 degrees.
  • A classification of object detection errors was proposed to aid in EIT instrument specification comparison.

Conclusions:

  • The developed automated test system offers a robust method for EIT device characterization.
  • The findings provide guidance for optimizing EIT device configurations and electrode placement.
  • The proposed error classification system enhances the comparability of different EIT instruments.