17 October 2022 Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography
Meng Wang, Shuo Zheng, Yanyan Shi, Yajun Lou
Author Affiliations +
Abstract

Purpose

Electrical impedance tomography (EIT) has shown its potential in the field of medical imaging. Physiological or pathological variation would cause the change of conductivity. EIT is favorable in reconstructing conductivity distribution inside the detected area. However, due to its ill-posed and nonlinear characteristics, reconstructed images suffer from low spatial resolution.

Approach

Tikhonov regularization method is a popular and effective approach for image reconstruction in EIT. Nevertheless, excessive smoothness is observed when reconstruction is conducted based on Tikhonov method. To improve Tikhonov-based reconstruction quality in EIT, an innovative hybrid iterative optimization method is proposed. An efficient alternating minimization algorithm is introduced to solve the optimization problem.

Results

To verify image reconstruction performance and anti-noise robustness of the proposed method, a series of simulation work and phantom experiments is carried out. Meanwhile, comparison is made with reconstruction results based on Landweber, Newton–Raphson, and Tikhonov methods. The reconstruction performance is also verified by quantitative comparison of blur radius and structural similarity values which further demonstrates the excellent performance of the proposed method.

Conclusions

In contrast to Landweber, Newton–Raphson, and Tikhonov methods, it is found that images reconstructed by the proposed method are more accurate. Even under the impact of noise, the proposed method outperforms comparison methods.

© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
Meng Wang, Shuo Zheng, Yanyan Shi, and Yajun Lou "Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography," Journal of Medical Imaging 9(5), 054503 (17 October 2022). https://doi.org/10.1117/1.JMI.9.5.054503
Received: 9 May 2022; Accepted: 23 September 2022; Published: 17 October 2022
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KEYWORDS
Image restoration

Signal to noise ratio

Electrodes

Tomography

Image quality

Numerical simulations

Performance modeling

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