Hybrid sensitivity‐correlation regularisation matrix for electrical impedance tomography

Borijindargoon, Narong; Ng, Boon Poh

doi:10.1049/iet-smt.2018.5267

Cited by 5 publications

(4 citation statements)

References 17 publications

(30 reference statements)

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“…enhanced the quality of the image by iteratively correcting the sensitivity matrix deviation, albeit at the expense of heightened computational demands [ 32 ]. The integration of two prior pieces of information for sensitivity matrix generation yields a well-balanced and robust performance, but the observed improvement in reconstruction results is not substantial [ 33 ]. Ding et al.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al enhanced the quality of the image by iteratively correcting the sensitivity matrix deviation, albeit at the expense of heightened computational demands [32]. The integration of two prior pieces of information for sensitivity matrix generation yields a well-balanced and robust performance, but the observed improvement in reconstruction results is not substantial [33]. Ding et al improved the spatial resolution of the image by considering the typically ignored second-order structure of the sensitivity matrix, yet this improvement is accompanied by limitations in robustness [34].…”

Section: Introductionmentioning

confidence: 99%

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Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography

Wang,

Sun,

2024

Sensors

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This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational complexity and avoids introducing extra parameters. Numerical simulations and experimental data verification highlight the effectiveness of SMDR. The proposed SMDR algorithm demonstrates higher accuracy and robustness compared to the typical Tikhonov regularization and the iterative penalty term-based regularization method (with an improvement of up to 0.1156 in correlation coefficient). Moreover, SMDR achieves a harmonious balance between image fidelity and sparsity, effectively addressing practical application requirements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography

Wang,

Sun,

2024

Sensors

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“…In addition, EIT has also been used to varying degrees in brain function, brain activity and tumor localization studies [ 6 , 7 , 8 ]. EIT is more advantageous in long-time monitoring with functional visualizations compared to computed tomography (CT) and magnetic resonance imaging (MRI) [ 9 , 10 ]. In industrial applications, it is mainly used for process monitoring, allowing real-time monitoring of the gas or liquid transported in the pipeline and a more accurate estimation of the flow rate [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Regularization Solver Guided FISTA for Electrical Impedance Tomography

Wang

Chen

Wang

et al. 2023

Sensors

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Electrical impedance tomography (EIT) is non-destructive monitoring technology that can visualize the conductivity distribution in the observed area. The inverse problem for imaging is characterized by a serious nonlinear and ill-posed nature, which leads to the low spatial resolution of the reconstructions. The iterative algorithm is an effective method to deal with the imaging inverse problem. However, the existing iterative imaging methods have some drawbacks, such as random and subjective initial parameter setting, very time consuming in vast iterations and shape blurring with less high-order information, etc. To solve these problems, this paper proposes a novel fast convergent iteration method for solving the inverse problem and designs an initial guess method based on an adaptive regularization parameter adjustment. This method is named the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA). The iterative solution process under the L1-norm regular constraint is derived in the LASSO problem. Meanwhile, the Nesterov accelerator is introduced to accelerate the gradient optimization race in the ISTA method. In order to make the initial guess contain more prior information and be independent of subjective factors such as human experience, a new adaptive regularization weight coefficient selection method is introduced into the initial conjecture of the FISTA iteration as it contains more accurate prior information of the conductivity distribution. The RS-FISTA method is compared with the methods of Landweber, CG, NOSER, Newton—Raphson, ISTA and FISTA, six different distributions with their optimal parameters. The SSIM, RMSE and PSNR of RS-FISTA methods are 0.7253, 3.44 and 37.55, respectively. In the performance test of convergence, the evaluation metrics of this method are relatively stable at 30 iterations. This shows that the proposed method not only has better visualization, but also has fast convergence. It is verified that the RS-FISTA algorithm is the better algorithm for EIT reconstruction from both simulation and physical experiments.

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“…The EIT technique has been proved to be potential and competitive in visualizing industrial process and monitoring biomedical disease (Ma et al, 2020; Pellegrini et al, 2020). Especially in the field of biomedical imaging, EIT is more advantageous than computed tomography (CT) and magnetic resonance imaging (MRI) (Borijindargoon and Ng, 2019; Nguyen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A two-stage image reconstruction strategy for electrical impedance tomography

Shi

Kong

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

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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.

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Hybrid sensitivity‐correlation regularisation matrix for electrical impedance tomography

Cited by 5 publications

References 17 publications

Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography

Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography

Regularization Solver Guided FISTA for Electrical Impedance Tomography

A two-stage image reconstruction strategy for electrical impedance tomography

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