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A Two-Stream Deep Imaging Method for Multifrequency Capacitively Coupled Electrical Resistance Tomography | IEEE Journals & Magazine | IEEE Xplore

A Two-Stream Deep Imaging Method for Multifrequency Capacitively Coupled Electrical Resistance Tomography


Abstract:

In this work, a novel deep imaging method is proposed for multifrequency capacitively coupled electrical resistance tomography (MFCCERT). A two-stream network consisting ...Show More

Abstract:

In this work, a novel deep imaging method is proposed for multifrequency capacitively coupled electrical resistance tomography (MFCCERT). A two-stream network consisting of a low-frequency stream and a high-frequency stream is developed according to the frequency characteristics of the interested impedance. Meanwhile, a cross-stream information intersection approach, which combines hyper-dense connection and gated channel transformation (GCT), is proposed to fuse the complementary information in the multifrequency impedance measurements. The multifrequency measurements of CCERT in the frequency range of 0.5–5 MHz are divided into the low-frequency band and the high-frequency band, which are taken as the inputs of the two streams of the network, respectively. With the proposed cross-stream information intersection approach, the useful features of the impedance in the same frequency band and the features of the impedance from the two frequency bands are fused. Experiments were carried out with the 12-electrode CCERT sensor to obtain the multifrequency impedance measurements. Both simulation and experimental data were used to test the developed two-stream network. Imaging results indicate that the proposed deep imaging method is effective. Compared with the single-stream Unet, the developed network has better information fusion capability and image reconstruction performance.
Published in: IEEE Sensors Journal ( Volume: 23, Issue: 5, 01 March 2023)
Page(s): 4362 - 4372
Date of Publication: 29 August 2022

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I. Introduction

Capacitively coupled electrical resistance tomography (CCERT) was proposed as a new electrical tomography (ET) technique [1], [2]. Although the sensor structure of CCERT is similar to that of electrical capacitance tomography (ECT), CCERT is regarded as an improved contactless alternative to electrical resistance tomography (ERT) because it focuses on the imaging of conductive materials. By referring to the contactless measurement idea of the capacitively coupled contactless conductivity detection (C4D) technique [3], [4], the electrodes of CCERT are not in contact with the measured medium, so the problems of ERT resulting from contact measurement can be avoided, that is, electrode polarization, electrochemical erosion, and so on. Currently, CCERT has shown good application potential in the measurement and monitoring of the gas–liquid two-phase flow. However, although previous work has verified the feasibility and effectiveness of CCERT, there is still a gap between its imaging quality and the requirement for practical application. Therefore, further research work is needed.

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1.
B. Wang, Y. Hu, H. Ji, Z. Huang and H. Li, "A novel electrical resistance tomography system based on C4D technique", IEEE Trans. Instrum. Meas., vol. 62, no. 5, pp. 1017-1024, May 2013.
2.
Y. Wang, B. Wang, Z. Huang, H. Ji and H. Li, "New capacitively coupled electrical resistance tomography (CCERT) system", Meas. Sci. Technol., vol. 29, no. 10, Oct. 2018.
3.
B. Gaš, M. Demjaněnko and J. Vacík, "High-frequency contactless conductivity detection in isotachophoresis", J. Chromatogr. A, vol. 192, no. 2, pp. 253-257, May 1980.
4.
P. Kuban and P. C. Hauser, "20th anniversary of axial capacitively coupled contactless conductivity detection in capillary electrophoresis", Trends Anal. Chem., vol. 102, pp. 311-321, Mar. 2018.
5.
W. Q. Yang and L. H. Peng, "Image reconstruction algorithms for electrical capacitance tomography", Meas. Sci. Technol., vol. 14, no. 1, pp. R1-R13, Dec. 2002.
6.
Z. Cui et al., "A review on image reconstruction algorithms for electrical capacitance/resistance tomography", Sensor Rev., vol. 36, no. 4, pp. 429-445, Sep. 2016.
7.
Y. Wang, X. He, Y. Jiang, B. Wang, H. Ji and Z. Huang, "New image reconstruction algorithm for CCERT: LBP plus Gaussian mixture model (GMM) clustering", Meas. Sci. Technol., vol. 32, no. 2, Nov. 2020.
8.
B. Zhou, C. Xu, D. Yang, S. Wang and X. Wu, "Nonlinear image reconstruction using a GA-ECT technique in electrical capacitance tomography", Flow Meas. Instrum., vol. 18, no. 5, pp. 285-294, Jun. 2007.
9.
F. Li, C. Tan, F. Dong and J. Jia, "V-net deep imaging method for electrical resistance tomography", IEEE Sensors J., vol. 20, no. 12, pp. 6460-6469, Jun. 2020.
10.
S. Martin and C. T. M. Choi, "Nonlinear electrical impedance tomography reconstruction using artificial neural networks and particle swarm optimization", IEEE Trans. Magn., vol. 52, no. 3, pp. 1-4, Mar. 2016.
11.
S. J. Hamilton and A. Hauptmann, "Deep D-bar: Real-time electrical impedance tomography imaging with deep neural networks", IEEE Trans. Med. Imag., vol. 37, no. 10, pp. 2367-2377, Apr. 2018.
12.
S. Ren et al., "A two-stage deep learning method for robust shape reconstruction with electrical impedance tomography", IEEE Trans. Instrum. Meas., vol. 69, no. 7, pp. 4887-4897, Jul. 2020.
13.
J. Xiang, Y. Dong and Y. Yang, "FISTA-Net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging", IEEE Trans. Med. Imag., vol. 40, no. 5, pp. 1329-1339, May 2021.
14.
S. Liu, Y. Huang, H. Wu, C. Tan and J. Jia, "Efficient multitask structure-aware sparse Bayesian learning for frequency-difference electrical impedance tomography", IEEE Trans. Ind. Informat., vol. 17, no. 1, pp. 463-472, Jan. 2021.
15.
Z. Chen, G. Ma, Y. Jiang, B. Wang and M. Soleimani, "Application of deep neural network to the reconstruction of two-phase material imaging by capacitively coupled electrical resistance tomography", Electronics, vol. 10, no. 9, Apr. 2021.
16.
S.-W. Huang, H.-M. Cheng and S.-F. Lin, "Improved imaging resolution of electrical impedance tomography using artificial neural networks for image reconstruction", Proc. 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), pp. 1551-1554, Jul. 2019.
17.
Y. Wu et al., "Shape reconstruction with multiphase conductivity for electrical impedance tomography using improved convolutional neural network method", IEEE Sensors J., vol. 21, no. 7, pp. 9277-9287, Apr. 2021.
18.
L. Zhu, Y. Jiang, Y. Li, W. Lu and M. Zhang, "Conductivity prediction and image reconstruction of complex-valued multi-frequency electrical capacitance tomography based on deep neural network", IEEE Trans. Instrum. Meas., vol. 71, Nov. 2021.
19.
I. Muttakin and M. Soleimani, "Interior void classification in liquid metal using multi-frequency magnetic induction tomography with a machine learning approach", IEEE Sensors J., vol. 21, no. 20, pp. 23289-23296, Oct. 2021.
20.
Y. Chen, K. Li and Y. Han, "Electrical resistance tomography with conditional generative adversarial networks", Meas. Sci. Technol., vol. 31, no. 5, May 2020.
21.
D. Smyl and D. Liu, "Optimizing electrode positions in 2-D electrical impedance tomography using deep learning", IEEE Trans. Instrum. Meas., vol. 69, no. 9, pp. 6030-6044, Sep. 2020.
22.
M. Capps and J. L. Mueller, "Reconstruction of organ boundaries with deep learning in the D-bar method for electrical impedance tomography", IEEE Trans. Biomed. Eng., vol. 68, no. 3, pp. 826-833, Mar. 2021.
23.
J. Yoo et al., "Deep learning diffuse optical tomography", IEEE Trans. Med. Imag., vol. 39, no. 4, pp. 877-887, Apr. 2020.
24.
S. Liu, R. Cao, Y. Huang, T. Ouypornkochagorn and J. Ji, "Time sequence learning for electrical impedance tomography using Bayesian spatiotemporal priors", IEEE Trans. Instrum. Meas., vol. 69, no. 9, pp. 6045-6057, Sep. 2020.
25.
J. Sun and W. Yang, "A dual-modality electrical tomography sensor for measurement of gas-oil-water stratified flows", Measurement, vol. 66, pp. 150-160, Apr. 2015.
26.
L. Zhu, L. Ma, Y. Li, Y. Yang and M. Zhang, "Linearization point and frequency selection for complex-valued electrical capacitance tomography", IEEE Trans. Instrum. Meas., vol. 70, Jun. 2021.
27.
C. L. Yang, A. Mohammed, Y. Mohamadou, T. I. Oh and M. Soleimani, "Complex conductivity reconstruction in multiple frequency electrical impedance tomography for fabric-based pressure sensor", Sensor Rev., vol. 35, no. 1, pp. 85-97, Jan. 2015.
28.
E. Malone, G. S. D. Santos, D. Holder and S. Arridge, "Multifrequency electrical impedance tomography using spectral constraints", IEEE Trans. Med. Imag., vol. 33, no. 2, pp. 340-350, Feb. 2014.
29.
O. Ronneberger, P. Fischer and T. Brox, "U-Net: Convolutional networks for biomedical image segmentation" in Medical Image Computing and Computer-Assisted Intervention–(MICCAI), Munich, Germany:Springer, pp. 234-241, Oct. 2015.
30.
K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition", Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 770-778, Jun. 2016.
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References is not available for this document.