Two-Dimensional Forward Modeling for Human Thorax Imaging Based on Electrical Impedance Tomography

Bader, Oumaima; Hafsa, Mariem; Amara, Najoua Essoukri Ben; Kanoun, Olfa

doi:10.1109/iwis54661.2021.9711764

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…Consequently, this technology has attracted significant attention and has been widely researched by numerous scholars [4]. Solving the reconstruction process of EIT involves addressing a challenging inverse problem that lacks a definitive solution [5,6]. In the field of electrical impedance, even slight changes in local resistivity result in minimal alterations in electrode potential, commonly referred to as the soft field effect.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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A MSARM-Based EIT Image Reconstruction Method

Dong,

Wang,

et al. 2024

Communications in Computer and Information Science

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Electrical impedance tomography image reconstruction for lung monitoring based on ensemble learning algorithms1

Al‐Bashir,

Al‐Bataiha,

Hafsa

et al. 2024

Healthcare Tech Letters

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Electrical impedance tomography (EIT) is a promising non‐invasive imaging technique that visualizes the electrical conductivity of an anatomic structure to form based on measured boundary voltages. However, the EIT inverse problem for the image reconstruction is nonlinear and highly ill‐posed. Therefore, in this work, a simulated dataset that mimics the human thorax was generated with boundary voltages based on given conductivity distributions. To overcome the challenges of image reconstruction, an ensemble learning method was proposed. The ensemble method combines several convolutional neural network models, which are the simple Convolutional Neural Network (CNN) model, AlexNet, AlexNet with residual block, and the modified AlexNet model. The ensemble models’ weights selection was based on average technique giving the best coefficient of determination (R2 score). The reconstruction quality is quantitatively evaluated by calculating the root mean square error (RMSE), the coefficient of determination (R2 score), and the image correlation coefficient (ICC). The proposed method's best performance is an RMSE of 0.09404, an R2 score of 0.926186, and an ICC of 0.95783 using an ensemble model. The proposed method is promising as it can construct valuable images for clinical EIT applications and measurements compared to previous studies.

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Two-Dimensional Forward Modeling for Human Thorax Imaging Based on Electrical Impedance Tomography

Cited by 6 publications

References 5 publications

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

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

A MSARM-Based EIT Image Reconstruction Method

Electrical impedance tomography image reconstruction for lung monitoring based on ensemble learning algorithms1

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