An image reconstruction algorithm of EIT based on pulmonary prior information

Wang, Huaxiang; Li, Hu; Wang, Jing; Li, Lu

doi:10.1007/s11460-009-0020-3

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…To take full advantage of ''soft-field'' characteristic, some prior information has been introduced into computation of the sensitivity map, which has proved that the sensing field is more uniform [12]. Besides that, normalization of the sensitivity map is an alternative method, which is widely used in electrical capacitance tomography (ECT) [13].…”

Section: Normalization Of the Sensitivity Mapmentioning

confidence: 99%

Maximum entropy regularization method for electrical impedance tomography combined with a normalized sensitivity map

Fan

Wang

2010

Flow Measurement and Instrumentation

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Section: Normalization Of the Sensitivity Mapmentioning

confidence: 99%

Maximum entropy regularization method for electrical impedance tomography combined with a normalized sensitivity map

Fan

Wang

2010

Flow Measurement and Instrumentation

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“…Zhang et al [23] used a wavelet function to fuse reconstruction images by iterative Landweber and conjugate gradient least square (CGLS) algorithms. Wang et al [24] used the conjugate gradient algorithm for EIT image reconstruction based on pulmonary prior information. Yan et al [25] presented a method of image reconstruction using the CG algorithm with regularized pre-processing factors.…”

mentioning

confidence: 99%

A Modified Regularization Approach to MIT Image Reconstruction Based on PSO-SA

Tian

Yang

Wang

et al. 2021

Preprint

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Background: Magnetic induction tomography (MIT) is a promising imaging modality of electrical tomography. The precision of image reconstruction algorithms in MIT places the key role in its application in industrial and biomedical fields. Image reconstruction in MIT is an ill-posed inverse problem. Regularization algorithms are effective to handle the ill-posed problem. However it is difficult to select its proper regularization parameter, larger or smaller regularization parameter will both effect on the quality of the reconstructed images.Methods: In this paper, firstly, the regularization principle of image reconstruction of MIT is analyzed. Then, we take the dimension of the Hessian matrix as the prior knowledge to obtain a novel penalty term, and add it to hybrid regularization algorithm, including Tikhonov and NOSER regularization, to propose a modified hybrid regularization algorithm. Secondly, selection for regularization parameters of image reconstruction algorithm is considered as a typical optimization problem, Particle swarm optimization with simulated annealing (PSO-SA) algorithm is used to solve the optimal solution.Results: Numberial experiments with six typical models and cerebral hemorrhage model were carried out. The correlation coefficient (CC), relative error (RE) and condition number of Hessian matrix are used as evaluation metrics to evaluate the effectiveness of the proposed method. The results obtained from the proposed method have lower REs and higher CCs compared to the other two methods. And the condition number of Hessian matrix in the proposed method is greatly reduced.Conclusions: The experimental results can prove the validity of the proposed method. The proposed method is capable of enhancing accuracy and noise immunity, which most closely coincides with the true conductivity distributions. Our method has better reconstructed quality compared with the other two methods, and it provides a theoretical reference for the development of application of the MIT technique in clinical decision application.

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Application of Particle Swarm Optimization with Simulated Annealing in MIT Regularization Image Reconstruction

et al. 2022

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Background and Objectives: Due to the soft-field effect of the electromagnetic field and the limit of detection, image reconstruction of magnetic induction tomography has to recover more complex electrical characteristics from very few signals. These cause a problem which have underdetermination, nonlinearity, and ill-posed characteristics, and therefore lead to many difficulties in finding its solution. Although many regularization image reconstruction methods exist, they are not suitable for MIT applications due to regularization parameter selection. The purpose of this paper is to study the principle of particle swarm optimization with simulated annealing, and to propose a regularization method for reconstruction, which will provide a new way to solve the MIT image problems. Methods and Models: Firstly, the regularization principle of image reconstruction of MIT will be analyzed. Then the hybrid regularization algorithm, including Tikhonov and NOSER regularization, will be developed, using the dimension of the Hessian matrix as a penalty term respecting the prior knowledge. PSO-SA algorithm will be applied to obtain an optimal solution for regularization parameters. Finally, six typical numerical models and approximately symmetrical cerebral hemorrhage models by COMSOL will be carried out, and the voltage signals obtained from the simulation will be used to verify the proposed reconstruction method. Results: Through the simulation results, the proposed imaging method has the average CC values of 0.9932, 0.8286 and the average RE values of 0.4982, 0.8320 for simple and complex models, respectively. Moreover, when the SNR changes from 55dB to 35dB, the CC value of the cerebral hemorrhage model reduced by 0.1034. The results demonstrate the effectiveness and high theoretical feasibility of the proposed method in MIT image reconstruction. Conclusions: This study indicates the potential application of PSO-SA algorithm in regularization imaging problem. Compared with traditional regularization imaging methods, the proposed method has the advantages of better accuracy, robustness and noise resistance, showing the certain application value in other similar ill-ness imaging problems.

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An image reconstruction algorithm of EIT based on pulmonary prior information

Cited by 3 publications

References 6 publications

Maximum entropy regularization method for electrical impedance tomography combined with a normalized sensitivity map

Maximum entropy regularization method for electrical impedance tomography combined with a normalized sensitivity map

A Modified Regularization Approach to MIT Image Reconstruction Based on PSO-SA

Application of Particle Swarm Optimization with Simulated Annealing in MIT Regularization Image Reconstruction

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