Image reconstruction method for electrical impedance tomography based on RBF and attention mechanism

Dong, Qinghe; Zhang, Yunjia; He, Qian; Xu, Chuanpei; Pan, Xipeng

doi:10.1016/j.compeleceng.2023.108826

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…A common practice involves considering a smooth estimation of TV regularization by employing Reg(σ) = λ TV ∑ i (M i σ) 2 + ω. The approximated solution for minimizing (8) can then be obtained. (20) where ω ∈ R + represents the smoothing parameter that can be adjusted in order to achieve optimal results.…”

Section: Regularized Gauss-newtonmentioning

confidence: 99%

“…With advancements in computer technology and numerical calculation methods, the solutions to the inverse problems have become more accurate and efficient [1]. Modern research on inverse problems typically involves techniques such as numerical simulation, inversion algorithms, and machine learning to analyze and derive known results and discover the underlying causes of problems [8]. However, researchers also face challenges in solving inverse problems in the presence of information interference and noise.…”

Section: Introductionmentioning

confidence: 99%

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A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography

Zhao,

Xu,

Zhu

et al. 2024

Applied Sciences

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Electrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc. Presently, most electrical impedance imaging methods are restricted to uniform domains, such as pixelated pictures. These algorithms rely on model learning-based image reconstruction techniques, which often necessitate interpolation and embedding if the fundamental imaging model is solved on a non-uniform grid. EIT technology still confronts several obstacles today, such as insufficient prior information, severe pathological conditions, numerous imaging artifacts, etc. In this paper, we propose a new electrical impedance tomography algorithm based on the graph convolutional neural network model. Our algorithm transforms the finite-element model (FEM) grid data from the ill-posed problem of EIT into a network graph within the graph convolutional neural network model. Subsequently, the parameters in the non-linear inverse problem of the EIT process are updated by using the improved Levenberg—Marquardt (ILM) method. This method generates an image that reflects the electrical impedance. The experimental results demonstrate the robust generalizability of our proposed algorithm, showcasing its effectiveness across different domain shapes, grids, and non-distributed data.

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Section: Regularized Gauss-newtonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%