New Image Reconstruction Algorithm for Capacitively Coupled Electrical Resistance Tomography

Tan, Wuhao; Wang, Baoliang; Huang, Zhiyao; Ji, Haifeng; Li, Haiqing

doi:10.1109/jsen.2017.2705108

Cited by 14 publications

(19 citation statements)

References 28 publications

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“…In 2017, the method consisting of a combination of the Levenberg-Marquardt (L-M) method and the simultaneous algebraic reconstruction technique (SART) was put forward. This method applied L-M for the initial guess and SART for final reconstruction [37]. Recently, the total variation (TV) algorithm with split Bregman iterations was used for CCERT reconstruction [15].…”

Section: Conventional Forward Modeling and Image Reconstruction Algorithm Of Ccertmentioning

confidence: 99%

Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography

Chen

Jiang

et al. 2021

Electronics

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A convolutional neural network (CNN)-based image reconstruction algorithm for two-phase material imaging is presented and verified with experimental data from a capacitively coupled electrical resistance tomography (CCERT) sensor. As a contactless version of electrical resistance tomography (ERT), CCERT has advantages such as no invasion, low cost, no radiation, and rapid response for two-phase material imaging. Besides that, CCERT avoids contact error of ERT by imaging from outside of the pipe. Forward modeling was implemented based on the practical circular array sensor, and the inverse image reconstruction was realized by a CNN-based supervised learning algorithm, as well as the well-known total variation (TV) regularization algorithm for comparison. The 2D, monochrome, 2500-pixel image was divided into 625 clusters, and each cluster was used individually to train its own CNN to solve the 16 classes classification problem. Inherent regularization for the assumption of binary materials enabled us to use a classification algorithm with CNN. The iterative TV regularization algorithm achieved a close state of the two-phase material reconstruction by its sparsity-based assumption. The supervised learning algorithm established the mathematical model that mapped the simulated resistance measurement to the pixel patterns of the clusters. The training process was carried out only using simulated measurement data, but simulated and experimental tests were both conducted to investigate the feasibility of applying a multi-layer CNN for CCERT imaging. The performance of the CNN algorithm on the simulated data is demonstrated, and the comparison between the results created by the TV-based algorithm and the proposed CNN algorithm with the real-world data is also provided.

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Section: Conventional Forward Modeling and Image Reconstruction Algorithm Of Ccertmentioning

confidence: 99%

Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography

Chen

Jiang

et al. 2021

Electronics

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“…Electrical impedance tomography (EIT) is another imaging technique widely used in medical imaging that can obtain complex impedance information [6]. In [7][8][9], a capacitively coupled ERT (CCERT) is proposed and applied to image the conductivity distribution without contact with liquids, which can be regarded as the combination of ECT and ERT because it applied ECT sensors to collect resistance information. In [10,11], phase information of complex admittance is used to reconstruct electrical parameter distribution, such as loss factor or permittivity.…”

Section: Index Terms-complex-valued Measurement Electrical Capacitance Tomography Image Reconstruction Multi-frequency Tomographymentioning

confidence: 99%

Linearization Point and Frequency Selection for Complex-Valued Electrical Capacitance Tomography

Zhu

Ma²,

et al. 2021

IEEE Trans. Instrum. Meas.

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“…In [12], a hybrid image reconstruction method with the combination of Tikhonov regularization and synchronous iterative reconstruction technique is presented for recovering conductivity distribution. In [13], image reconstruction in CCERT is implemented by combining Levenberg-Marquardt (L-M) method with synchronous algebraic reconstruction technique.…”

Section: Introductionmentioning

confidence: 99%

Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography

Shi

Liao

Wang

et al. 2021

Flow Measurement and Instrumentation

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New Image Reconstruction Algorithm for Capacitively Coupled Electrical Resistance Tomography

Cited by 14 publications

References 28 publications

Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography

Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography

Linearization Point and Frequency Selection for Complex-Valued Electrical Capacitance Tomography

Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography

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