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Data preprocessing methods for electrical impedance tomography: a review.

Zeying Wang¹, Shihong Yue¹, Huaxiang Wang¹

¹School of Electrical Information and Automation, Tianjin University, Tianjin 300072, People's Republic of China.

Physiological Measurement

|October 5, 2020

Summary

This summary is machine-generated.

Preprocessing methods enhance electrical impedance tomography (EIT) image quality. An optimal method exists for each EIT reconstruction algorithm, though most methods struggle with Tikhonov regularization.

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Area of Science:

Electrical impedance tomography (EIT)
Image reconstruction
Signal processing

Background:

EIT is a valuable technique for industrial monitoring and clinical diagnosis.
Spatial resolution in EIT is limited by the 'soft field' effect and ill-posed problems.
Preprocessing measured data or sensitivity maps can mitigate these limitations.

Purpose of the Study:

To review and evaluate seven data preprocessing methods for EIT.
To compare the image quality achieved with different preprocessing techniques via simulations.
To determine the optimal preprocessing method for various EIT reconstruction algorithms.

Main Methods:

Simulation-based evaluation of seven distinct data preprocessing techniques for EIT.
Comparative analysis of reconstructed EIT image quality.
Assessment of method applicability and synergistic effects.

Main Results:

All reviewed preprocessing methods improve EIT reconstructed image quality to varying degrees.
An optimal preprocessing method is identifiable for any given EIT reconstruction algorithm.
Most preprocessing methods show limited efficacy with the Tikhonov regularization algorithm.

Conclusions:

Preprocessing is crucial for enhancing EIT image quality.
Selection of the optimal preprocessing method depends on the specific reconstruction algorithm.
Further research may be needed to improve preprocessing compatibility with algorithms like Tikhonov regularization.