Influence of regularization in image reconstruction in electrical impedance tomography

被引:1
|
作者
Queiroz, J. L. L.
机构
关键词
RESISTANCE TOMOGRAPHY; OPTICAL TOMOGRAPHY; BUBBLE-COLUMNS; EIT IMAGES; FLOW; RESOLUTION; PROJECT; REACTOR;
D O I
10.1088/1742-6596/407/1/012006
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The purpose of the application of electrical impedance tomography is to obtain images of areas that are difficult to access inside of the chest and other parts of the human being. Today, this has been applied in other areas of engineering with a view to investigate phenomena for which it is difficult to obtain data for robust research. Electrical Impedance Tomography (EIT) of an inverse problem is nonlinear and ill-conditioned. This requires a careful theoretical approach and practice to get good images. To enhance the images, it is important to be sensitive to various parameters that influence the process of image reconstruction, such as the measured voltage and the current density injected into the electrodes. The impedance contact and current density are both high in a point electrode. To reduce this, a large electrode modeled with a Finite Element Method (FEM) is used. A reduced numbers iterations is found when larges electrodes are used. When FEM models are used the performance of the electrical impedance tomography reconstruction algorithm can be improved.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Image Reconstruction Based on 11 Regularization for Electrical Impedance Tomography (EIT)
    Wang, Qi
    Wang, Huaxiang
    [J]. 2011 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC), 2011, : 1233 - 1237
  • [2] Influence of Boundary Deformation on Image Reconstruction in Electrical Impedance Tomography
    Wang, Lei
    Deng, Juan
    Zhao, Shu
    Wang, Hong
    Sha, Hong
    Wang, Yan
    [J]. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2020, 10 (10) : 2274 - 2278
  • [3] Reducing negative effects of quadratic norm regularization on image reconstruction in electrical impedance tomography
    Javaherian, Ashkan
    Movafeghi, Amir
    Faghihi, Reza
    [J]. APPLIED MATHEMATICAL MODELLING, 2013, 37 (08) : 5637 - 5652
  • [4] Temporal image reconstruction in electrical impedance tomography
    Adler, Andy
    Dai, Tao
    Lionheart, William R. B.
    [J]. PHYSIOLOGICAL MEASUREMENT, 2007, 28 (07) : S1 - S11
  • [5] A reconstruction algorithm for electrical impedance tomography based on sparsity regularization
    Jin, Bangti
    Khan, Taufiquar
    Maass, Peter
    [J]. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2012, 89 (03) : 337 - 353
  • [6] Image reconstruction based on L1 regularization and projection methods for electrical impedance tomography
    Wang, Qi
    Wang, Huaxiang
    Zhang, Ronghua
    Wang, Jinhai
    Zheng, Yu
    Cui, Ziqiang
    Yang, Chengyi
    [J]. REVIEW OF SCIENTIFIC INSTRUMENTS, 2012, 83 (10):
  • [7] Dual-Modal Image Reconstruction for Electrical Impedance Tomography With Overlapping Group Lasso and Laplacian Regularization
    Liu, Zhe
    Gu, Hengjia
    Chen, Zhou
    Bagnaninchi, Pierre
    Yang, Yunjie
    [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2023, 70 (08) : 2362 - 2373
  • [8] Electrical Impedance Tomography Reconstruction using Hybrid Variation Regularization Algorithm
    Zhang, Shuai
    Guo, Yunge
    Zhang, Xueying
    Xu, Guizhi
    [J]. 2016 IEEE CONFERENCE ON ELECTROMAGNETIC FIELD COMPUTATION (CEFC), 2016,
  • [9] Logistic regression in image reconstruction in electrical impedance tomography
    Kozlowski, Edward
    Rymarczyk, Tomasz
    Klosowski, Grzegorz
    Cieplak, Tomasz
    [J]. PRZEGLAD ELEKTROTECHNICZNY, 2020, 96 (05): : 95 - 98
  • [10] UNet model in image reconstruction for electrical impedance tomography
    Maciura, Lukasz
    Wojcik, Dariusz
    Rosa, Wojciech
    Rymarczyk, Tomasz
    Maj, Michal
    [J]. PRZEGLAD ELEKTROTECHNICZNY, 2022, 98 (04): : 123 - 126