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Related Experiment Videos

Electrical impedance tomography using the extended Kalman filter.

Flávio Celso Trigo1, Raul Gonzalez-Lima, Marcelo Brito Passos Amato

  • 1Department of Mechanical Engineering, Polytechnic School, University of São Paulo-SP, R. da Consolação 3064-171A, 01416-000 São Paulo, Brazil. ftrigo@netpoint.com.br

IEEE Transactions on Bio-Medical Engineering
|January 16, 2004
PubMed
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This study introduces an advanced algorithm for electrical impedance tomography (EIT) to accurately map conductivity. The method shows reliable performance even with significant measurement noise, aiding ventilation management.

Area of Science:

  • Biomedical Engineering
  • Electrical Engineering
  • Medical Imaging

Background:

  • Electrical Impedance Tomography (EIT) is a non-invasive imaging technique.
  • Accurate conductivity/resistivity distribution estimation is crucial for EIT applications.
  • Current methods face challenges with noise and wide conductivity ranges.

Purpose of the Study:

  • To develop and validate an algorithm for estimating conductivity/resistivity distribution using EIT.
  • To assess the algorithm's performance under varying noise levels and with spatial prior information.
  • To provide data for optimizing air volume and pressure in artificial ventilation.

Main Methods:

  • Utilized the extended Kalman filter to solve the inverse problem in EIT.
  • Employed a two-phase identification procedure for conductivity/resistivity estimation.

Related Experiment Videos

  • Validated the algorithm using numerical simulations and experimental evaluation with a saline phantom and a glass object.

    • Main Results:

    • The algorithm accurately estimates conductivity/resistivity over a wide range.
    • With up to 5% measurement noise, conductivity estimates achieved 7% accuracy.
    • Experimental evaluation demonstrated successful imaging of resistivity changes and electrode contact impedances.

    Conclusions:

    • The proposed EIT algorithm effectively estimates conductivity/resistivity distributions.
    • The algorithm is robust to measurement noise and provides accurate results.
    • This technique has potential applications in monitoring and controlling artificial ventilation.