A new image reconstruction strategy for capacitively coupled electrical impedance tomography

被引:3
|
作者
Wu, Yimin [1 ]
Jiang, Yandan [1 ]
Ji, Haifeng [1 ]
Wang, Baoliang [1 ]
Huang, Zhiyao [1 ]
Soleimani, Manuchehr [2 ]
机构
[1] Zhejiang Univ, Coll Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
[2] Univ Bath, Engn Tomog Lab ETL, Dept Elect & Elect Engn, Bath BA2 7AY, England
基金
中国国家自然科学基金;
关键词
electrical resistance tomography (ERT); capacitively coupled electrical impedance tomography (CCEIT); sensitivity matrix; image reconstruction; image fusion; SENSOR;
D O I
10.1088/1361-6501/ad0f10
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Capacitively coupled electrical impedance tomography (CCEIT) is an attractive improvement of electrical resistance tomography (ERT) that offers contactless measurement and utilizes both the real and imaginary parts of the impedance for monitoring conductive gas-liquid two-phase flows in the industry. The conventional CCEIT adopts the finite element method under the benchmark of conductive liquid background to obtain the sensitivity matrices, which has been validated effective in ERT for the usage of the real part information. However, few researches on the usage of the imaginary part information of the conductive fluid have been reported. More research work should be undertaken to seek the most effective sensitivity calculation benchmark for the imaginary part utilization in CCEIT. In this work, the usage of the imaginary part information under different sensitivity calculation benchmarks is studied and a new image reconstruction strategy is proposed for CCEIT. By comparing the imaginary part sensitivity matrices and the corresponding imaging performance under different backgrounds, the benchmark that can make better use of the imaginary part information is determined. With the determined benchmark, a new image reconstruction strategy of CCEIT, which utilizes the respective effective benchmarks for the image reconstruction of the two parts of the fluid impedance, and employs a novel hybrid image fusion method to obtain the final image, is presented. Research results show that the benchmark of non-conductive gas background is more effective for the usage of the imaginary part information of the conductive gas-liquid two-phase flow. And the experimental results demonstrate the effectiveness of the proposed strategy in obtaining high-quality images. Compared with the conventional image reconstruction strategy of CCEIT, the proposed strategy has better imaging performance. This research provides valuable experience in utilizing the imaginary part information of the fluid impedance and lays a good foundation for the further development of CCEIT.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Advances of deep learning in electrical impedance tomography image reconstruction
    Zhang, Tao
    Tian, Xiang
    Liu, XueChao
    Ye, JianAn
    Fu, Feng
    Shi, XueTao
    Liu, RuiGang
    Xu, CanHua
    FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2022, 10
  • [42] Image Reconstruction Based on Structured Sparsity for Electrical Impedance Tomography
    Wang, Qi
    He, Jing
    Wang, Jianming
    Li, Xiuyan
    Duan, Xiaojie
    2018 2ND INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND BIOINFORMATICS (ICBEB 2018), 2018, : 42 - 48
  • [43] IMAGE-RECONSTRUCTION PROBLEMS IN ELECTRICAL-IMPEDANCE TOMOGRAPHY
    BARBER, DC
    CLINICAL PHYSICS AND PHYSIOLOGICAL MEASUREMENT, 1990, 11 (02): : 181 - 182
  • [44] LEARNING SPARSIFYING TRANSFORMS FOR IMAGE RECONSTRUCTION IN ELECTRICAL IMPEDANCE TOMOGRAPHY
    Yang, Kaiyi
    Borijindargoon, Narong
    Ng, Boon Poh
    Ravishankar, Saiprasad
    Wen, Bihan
    2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 1405 - 1409
  • [45] Image reconstruction incorporated with the skull inhomogeneity for electrical impedance tomography
    Ni, Ansheng
    Dong, Xiuzhen
    Yang, Guosheng
    Fu, Feng
    Tang, Chi
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2008, 32 (05) : 409 - 415
  • [46] Stochastic Optimization Approaches to Image Reconstruction in Electrical Impedance Tomography
    Boo, Chang-Jin
    Kim, Ho-Chan
    Kang, Min-Jae
    Lee, Kwang Y.
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2010, PT 2, PROCEEDINGS, 2010, 6017 : 99 - 109
  • [47] Sparse image reconstruction of intracerebral hemorrhage with electrical impedance tomography
    Shi, Yanyan
    Wu, Yuehui
    Wang, Meng
    Tian, Zhiwei
    Kong, Xiaolong
    He, Xiaoyue
    JOURNAL OF MEDICAL IMAGING, 2021, 8 (01)
  • [48] Adaptive Kaczmarz method for image reconstruction in electrical impedance tomography
    Li, Taoran
    Kao, Tzu-Jen
    Isaacson, David
    Newell, Jonathan C.
    Saulnier, Gary J.
    PHYSIOLOGICAL MEASUREMENT, 2013, 34 (06) : 595 - 608
  • [49] Influence of Boundary Deformation on Image Reconstruction in Electrical Impedance Tomography
    Wang, Lei
    Deng, Juan
    Zhao, Shu
    Wang, Hong
    Sha, Hong
    Wang, Yan
    JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2020, 10 (10) : 2274 - 2278
  • [50] Electrical Impedance Tomography Image Reconstruction Based on Neural Networks
    Bianchessi, Andre
    Akamine, Rodrigo H.
    Duran, Guilherme C.
    Tanabi, Naser
    Sato, Andre K.
    Martins, Thiago C.
    Tsuzuki, Marcos S. G.
    IFAC PAPERSONLINE, 2020, 53 (02): : 15946 - 15951