Open AccessRobust imaging using electrical impedance tomography: review of current tools
Author(s) -
Benoît Brazey,
Yassine Haddab,
Nabil Zemiti
Publication year - 2022
Publication title -
proceedings - royal society. mathematical, physical and engineering sciences
Language(s) - English
Resource type - Journals
eISSN - 1471-2946
pISSN - 1364-5021
DOI - 10.1098/rspa.2021.0713
Subject(s) - electrical impedance tomography , robustness (evolution) , computer science , electrical resistivity tomography , data acquisition , inverse problem , tomography , iterative reconstruction , noise (video) , electrical impedance , artificial intelligence , algorithm , computer vision , image (mathematics) , engineering , electrical engineering , electrical resistivity and conductivity , mathematics , physics , mathematical analysis , biochemistry , chemistry , optics , gene , operating system
Electrical impedance tomography (EIT) is a medical imaging technique with many advantages and great potential for development in the coming years. Currently, some limitations of EIT are related to the ill-posed nature of the problem. These limitations are translated on a practical level by a lack of genericity of the developed tools. In this paper, the main robust data acquisition and processing tools for EIT proposed in the scientific literature are presented. Their relevance and potential to improve the robustness of EIT are analysed, in order to conclude on the feasibility of a robust EIT tool capable of providing resistivity or difference of resistivity mapping in a wide range of applications. In particular, it is shown that certain measurement acquisition tools and algorithms, such as faulty electrode detection algorithm or particular electrode designs, can ensure the quality of the acquisition in many circumstances. Many algorithms, aiming at processing acquired data, are also described and allow to overcome certain difficulties such as an error in the knowledge of the position of the boundaries or the poor conditioning of the inverse problem. They have a strong potential to faithfully reconstruct a quality image in the presence of disturbances such as noise or boundary modelling error.