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Electrical impedance tomography reconstruction algorithm based on general inversion theory and finite element method.

T Mengxing1, D Xiuzhen, Q Mingxin

  • 1Biomedical Engineering Department, Fourth Military Medical University, Xian, Shaanxi, People's Republic of China. bmee@fmmu.edu.cn

Medical & Biological Engineering & Computing
|April 13, 1999
PubMed
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A modified general inversion algorithm (MGIA) improves electrical impedance tomography (EIT) image reconstruction by reducing noise and errors. This enhanced algorithm provides more accurate dynamic impedance images, even with noisy data.

Area of Science:

  • Electrical Impedance Tomography (EIT)
  • Biomedical Imaging
  • Computational Electromagnetics

Background:

  • Electrical Impedance Tomography (EIT) is a non-invasive imaging technique.
  • Conventional EIT reconstruction algorithms face challenges with noise and model complexity.
  • The General Inversion Algorithm (GIA) is a strict EIT reconstruction method.

Purpose of the Study:

  • To develop a noise-robust EIT reconstruction algorithm.
  • To improve the accuracy of dynamic impedance imaging.
  • To enhance the practicality of EIT algorithms for larger models.

Main Methods:

  • Modified General Inversion Algorithm (MGIA) by attenuating the forward matrix condition number.
  • Implementation using an improved Finite Element Method (FEM) scheme.

Related Experiment Videos

  • Validation through computer simulation and physical phantom experiments.

    • Main Results:

    • MGIA demonstrated smaller reconstruction errors compared to equipotential-back-projection and filtered spectral expansion algorithms.
    • The algorithm successfully reconstructed complex models with 0.1% white noise.
    • MGIA is practical for larger FEM models (248 elements).

    Conclusions:

    • The MGIA offers superior noise performance and accuracy for EIT reconstruction.
    • This enhanced algorithm is more robust and practical for complex EIT applications.
    • Further improvements for MGIA are under investigation.