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Related Experiment Video

Updated: Jun 17, 2026

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography

Published on: January 30, 2016

PEAS: parametric EIT analysis software, a software to perform analyses on electrical impedance tomography data.

Claas Händel^1,2

¹Department of Anesthesiology and Intensive Care Medicine, University Medical Center Schleswig-Holstein, Kiel, Germany.

Physiological Measurement

|March 3, 2026

Summary

We developed Parametric EIT Analysis Software (PEAS) to simplify electrical impedance tomography (EIT) data analysis. This user-friendly platform standardizes workflows, enhancing EIT

Keywords:

analysis software electrical impedance tomography open-source software template-based analysis workflow standardization

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Related Experiment Videos

Last Updated: Jun 17, 2026

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography

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Published on: January 30, 2016

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

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Published on: July 15, 2021

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Published on: September 6, 2024

Area of Science:

Medical Imaging
Biomedical Engineering
Computational Physiology

Background:

Electrical impedance tomography (EIT) is valuable for regional ventilation assessment.
EIT data analysis is complex due to varied data formats, protocols, and research goals.
A need exists for standardized and user-friendly EIT data analysis tools.

Purpose of the Study:

To develop a modular, user-friendly software platform for simplifying and standardizing EIT data analysis.
To lower the technical barrier for EIT application in research and clinical settings.

Main Methods:

Developed Parametric EIT Analysis Software (PEAS), a modular platform using configurable, template-driven workflows.
PEAS supports raw voltage data with image reconstruction and pre-reconstructed images.
Integrated temporal detectors for breathing cycles/maneuvers and reusable analysis components via a graphical user interface.

Main Results:

PEAS supports multiple vendor-specific data formats (raw voltage and reconstructed images).
Automated detection of breathing cycles and respiratory maneuvers.
Over 40 generic building blocks for customized analysis pipelines; workflows execute in seconds on standard hardware.
User study indicated PEAS is easy to learn and operate.

Conclusions:

PEAS provides a standardized, extensible, and user-friendly environment for EIT data analysis.
The platform enhances reproducibility, interoperability, and adoption of EIT for physiological monitoring.
PEAS reduces technical barriers, facilitating EIT's use in research and clinical practice.