A two-stage image reconstruction strategy for electrical impedance tomography

Abstract

Electrical impedance tomography (EIT) is a potential and promising tomographic technique. Based on a reconstruction strategy, conductivity distribution can be imaged by processing boundary measurements. It should be noticed that the process of image reconstruction involves the solution of a nonlinear ill-posed inverse problem. To tackle this problem, a novel two-stage image reconstruction strategy is proposed in this work. It combines the advantages of total generalized variation regularization method and tight wavelet approach. The solution of the proposed method is acquired by employing alternating minimization algorithm and spilt Bregman algorithm. In the numerical simulation, reconstruction of five models is studied. Aside from the visual observation, we have also validated the proposed method with quantitative comparison. Meanwhile, the impact of noise on the reconstruction is considered. Furthermore, the proposed method is evaluated by phantom experimental data. The simulation and experimental results have demonstrated the superior performance of the proposed method in visualizing conductivity distribution.

Reconstruction of conductivity distribution variation with Tikhonov and wavelet frame combined method for electrical impedance tomography

“…Although this technique has lots of advantages in contrast to other methods, the number of independent measurements is much smaller than the number of pixels in the perceived area. As a result, image reconstruction from boundary measurement is mathematically a seriously ill-posed inverse problem (Seo et al, 2019; Shi et al, 2021). In addition, impacts of noise or measurement error pose a great challenge for accurate image reconstruction (Bai et al, 2022).…”