Image Reconstruction of Electrical Impedance Tomography Based on Optical Image-Guided Group Sparsity

Liu, Zhe; Yang, Yunjie

doi:10.1109/jsen.2021.3104967

Cited by 13 publications

(7 citation statements)

References 36 publications

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“…For instance, Gong et al proposed to incorporate structural information from CT or preliminary EIT reconstructions into the EIT inversion process using group lasso [22]. Liu et al reported a group lasso based dual-modal reconstruction method and their grouping method is based on the semantic segmentation of the prior image [23]. The segmentation algorithm needs to be carefully chosen and tuned for a specific application, and its complexity is even higher than the reconstruction algorithm in some cases.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

et al. 2023

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The intrinsic issue of low spatial resolution of Electrical Impedance Tomography (EIT) is a long-standing challenge that hinders the capability of performing quantitative analysis based on EIT image. Our recent work demonstrates an impedance-optical dual-modal imaging framework and a deep learning model named Multi-Scale Feature Cross Fusion Network (MSFCF-Net) to realize information fusion and high-quality EIT image reconstruction. However, this framework's performance is limited by the accuracy of the mask image obtained from an auxiliary imaging modality. This paper further proposes a two-stage deep neural network, which is the enhanced version of the MSFCF-Net (named En-MSFCF-Net), to automatically improve mask image and conduct information fusion and image reconstruction. Compared to MSFCF-Net, En-MSFCF-Net demonstrates the superior ability to correct the inaccurate mask image, leading to a more accurate conductivity estimation. Furthermore, the En-MSFCF-Net also maintains the best shape preservation and conductivity prediction accuracy among given learning-based and modelbased algorithms. Both qualitative and quantitative results indicate that En-MSFCF-Net could make dual-modal imaging more robust in real-world situations.

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Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

et al. 2023

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“…With L 2 -norm as the penalty term, Tikhonov regularization method has been widely used due to its advantages of simple and fast implementation. However, this method fails to maintain sharp edge information, and over-smoothness is observed in the reconstructed images (Liu and Yang, 2021). Thus, the reconstruction quality of this method needs improvement.…”

Section: Image Reconstruction Based On the Proposed Methodsmentioning

confidence: 99%

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

Shi

Yuhang

Wang

et al. 2023

Transactions of the Institute of Measurement and Control

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Electrical impedance tomography (EIT) is receiving considerable research interest in a variety of applications. With this technique, it is possible to reconstruct conductivity distribution inside the detected region. Note that reconstruction of conductivity distribution is an ill-posed inverse problem. To deal with this problem, Tikhonov regularization method has been preferred. However, this method suffers from poor reconstruction quality. In this work, a novel approach which combines Tikhonov regularization method with wavelet frame is proposed for image reconstruction of EIT. The proposed method is superior to the popular Tikhonov regularization method in enforcing sparse of the solution and enhancing sharp feature. Simulation work has been conducted to validate the performance of the proposed method. Compared with Landweber method, total variation (TV) regularization method, and Tikhonov regularization method, reconstructions of six different models show that images reconstructed by the proposed method have better quality and are more robust to noise. In addition, image reconstruction is also performed based on phantom experimental data. The results further demonstrate that the proposed method outperforms other three methods since the inclusion has been more accurately reconstructed, and there are almost no artifacts in the reconstructed images.

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“…With boundary data measured, conductivity distribution inside the detected object can be reconstructed via image reconstruction. [6][7][8] At present, EIT has become one of the fastest-developing electrical tomography techniques and shows great potential in various medical imaging fields, such as brain imaging, pulmonary monitoring, and breast cancer detection. [9][10][11] In EIT, a variety of studies have been conducted, such as quantization error and electrode position errors in the modeling.…”

Section: Introductionmentioning

confidence: 99%

Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography

et al. 2022

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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.

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Image Reconstruction of Electrical Impedance Tomography Based on Optical Image-Guided Group Sparsity

Cited by 13 publications

References 36 publications

Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

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

Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography

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