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Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network.

Guanghua Wang1, Di Feng1, Wenlai Tang1,2,3,4

  • 1Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China.

Micromachines
|July 27, 2022
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This summary is machine-generated.

This study introduces a novel Grey Wolf Optimized Radial Basis Function Neural Network (GWO-RBFNN) to enhance electrical impedance tomography (EIT) image reconstruction. The GWO-RBFNN significantly improves image resolution and accuracy, outperforming existing methods.

Area of Science:

  • Medical Imaging
  • Computational Intelligence
  • Biomedical Engineering

Background:

  • Electrical impedance tomography (EIT) is a promising non-invasive imaging technique.
  • EIT image reconstruction is challenging due to its non-linear and ill-posed nature.
  • Improving EIT image quality is crucial for effective clinical monitoring.

Purpose of the Study:

  • To enhance the accuracy and resolution of EIT image reconstruction.
  • To introduce a novel optimization algorithm for EIT image reconstruction.
  • To evaluate the performance of the proposed method against existing techniques.

Main Methods:

  • A Grey Wolf Optimized Radial Basis Function Neural Network (GWO-RBFNN) was developed.
  • The Grey Wolf Algorithm optimized the weights of the RBFNN for improved mapping.
Keywords:
electrical impedance tomographygrey wolf optimization algorithmimage reconstructionradial basis function neural networks

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  • Simulations were conducted using COMSOL and MATLAB with a 16-electrode EIT system.

    • Main Results:

    • The GWO-RBFNN achieved an image correlation coefficient (ICC) of 0.9551 on a test set.
    • The method demonstrated robustness against Gaussian white noise, with ICCs of 0.8966, 0.9197, and 0.9319 at 30, 40, and 50 dB noise levels.
    • The proposed GWO-RBFNN significantly outperformed standard Landweber and RBFNN methods.

    Conclusions:

    • The GWO-RBFNN is a superior approach for EIT image reconstruction.
    • This method offers enhanced accuracy and resolution, particularly in noisy conditions.
    • The GWO-RBFNN holds potential for advancing clinical applications of EIT.