A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography
Electrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/595 |
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| author | Ruwen Zhao Chuanpei Xu Zhibin Zhu Wei Mo |
| author_facet | Ruwen Zhao Chuanpei Xu Zhibin Zhu Wei Mo |
| author_sort | Ruwen Zhao |
| collection | DOAJ |
| description | Electrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc. Presently, most electrical impedance imaging methods are restricted to uniform domains, such as pixelated pictures. These algorithms rely on model learning-based image reconstruction techniques, which often necessitate interpolation and embedding if the fundamental imaging model is solved on a non-uniform grid. EIT technology still confronts several obstacles today, such as insufficient prior information, severe pathological conditions, numerous imaging artifacts, etc. In this paper, we propose a new electrical impedance tomography algorithm based on the graph convolutional neural network model. Our algorithm transforms the finite-element model (FEM) grid data from the ill-posed problem of EIT into a network graph within the graph convolutional neural network model. Subsequently, the parameters in the non-linear inverse problem of the EIT process are updated by using the improved Levenberg—Marquardt (ILM) method. This method generates an image that reflects the electrical impedance. The experimental results demonstrate the robust generalizability of our proposed algorithm, showcasing its effectiveness across different domain shapes, grids, and non-distributed data. |
| first_indexed | 2024-03-08T09:59:21Z |
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| id | doaj.art-e87d4cf27b1f4302833e759cd596f73b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:59:21Z |
| publishDate | 2024-01-01 |
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| spelling | doaj.art-e87d4cf27b1f4302833e759cd596f73b2024-01-29T13:42:59ZengMDPI AGApplied Sciences2076-34172024-01-0114259510.3390/app14020595A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance TomographyRuwen Zhao0Chuanpei Xu1Zhibin Zhu2Wei Mo3Key Laboratory of Automatic Detecting Technology and Instruments, School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, ChinaKey Laboratory of Automatic Detecting Technology and Instruments, School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, ChinaKey Laboratory of Automatic Detecting Technology and Instruments, School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, ChinaElectrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc. Presently, most electrical impedance imaging methods are restricted to uniform domains, such as pixelated pictures. These algorithms rely on model learning-based image reconstruction techniques, which often necessitate interpolation and embedding if the fundamental imaging model is solved on a non-uniform grid. EIT technology still confronts several obstacles today, such as insufficient prior information, severe pathological conditions, numerous imaging artifacts, etc. In this paper, we propose a new electrical impedance tomography algorithm based on the graph convolutional neural network model. Our algorithm transforms the finite-element model (FEM) grid data from the ill-posed problem of EIT into a network graph within the graph convolutional neural network model. Subsequently, the parameters in the non-linear inverse problem of the EIT process are updated by using the improved Levenberg—Marquardt (ILM) method. This method generates an image that reflects the electrical impedance. The experimental results demonstrate the robust generalizability of our proposed algorithm, showcasing its effectiveness across different domain shapes, grids, and non-distributed data.https://www.mdpi.com/2076-3417/14/2/595electrical impedance tomographygraph neural networkimage reconstructionLevenberg–Marquardt |
| spellingShingle | Ruwen Zhao Chuanpei Xu Zhibin Zhu Wei Mo A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography Applied Sciences electrical impedance tomography graph neural network image reconstruction Levenberg–Marquardt |
| title | A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography |
| title_full | A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography |
| title_fullStr | A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography |
| title_full_unstemmed | A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography |
| title_short | A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography |
| title_sort | graph neural network approach with improved levenberg marquardt for electrical impedance tomography |
| topic | electrical impedance tomography graph neural network image reconstruction Levenberg–Marquardt |
| url | https://www.mdpi.com/2076-3417/14/2/595 |
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