Imaging system design based on electromagnetic tomography for high conductivity medium reconstruction

Electromagnetic tomography (EMT) is used to create tomographic images of the electrical properties of conducting material based on electromagnetic measurements from coils evenly distributed around the imaging region. EMT is widely used in industrial and biomedical fields for which it offers the adva...

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Published inReview of scientific instruments Vol. 94; no. 3; pp. 034706 - 34716
Main Authors Liu, Xianglong, Wang, Ying, Li, Danyang
Format Journal Article
LanguageEnglish
Published United States American Institute of Physics 01.03.2023
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Abstract Electromagnetic tomography (EMT) is used to create tomographic images of the electrical properties of conducting material based on electromagnetic measurements from coils evenly distributed around the imaging region. EMT is widely used in industrial and biomedical fields for which it offers the advantages of being non-contact, fast, and non-radiative. Most EMT measurement systems are implemented with commercial instruments, such as impedance analyzers and lock-in amplifiers, which are bulky and inconvenient for portable detection devices. In order to improve the portability and extensibility, a purpose-built flexible and modularized EMT system is presented in this paper. The hardware system consists of six parts: the sensor array, signal conditioning module, lower computer module, data acquisition module, excitation signal module, and the upper computer. The complexity of the EMT system is reduced by a modularized design. The sensitivity matrix is calculated by the perturbation method. The split Bregman algorithm is applied to solve the L1 norm regularization problem. The effectiveness and advantages of the proposed method are verified by numerical simulations. The average signal to noise ratio of the EMT system is 48 dB. Experimental results verified that the reconstructed images can show the number and positions of the imaging objects, demonstrating the feasibility and effectiveness of the novel imaging system design.
AbstractList Electromagnetic tomography (EMT) is used to create tomographic images of the electrical properties of conducting material based on electromagnetic measurements from coils evenly distributed around the imaging region. EMT is widely used in industrial and biomedical fields for which it offers the advantages of being non-contact, fast, and non-radiative. Most EMT measurement systems are implemented with commercial instruments, such as impedance analyzers and lock-in amplifiers, which are bulky and inconvenient for portable detection devices. In order to improve the portability and extensibility, a purpose-built flexible and modularized EMT system is presented in this paper. The hardware system consists of six parts: the sensor array, signal conditioning module, lower computer module, data acquisition module, excitation signal module, and the upper computer. The complexity of the EMT system is reduced by a modularized design. The sensitivity matrix is calculated by the perturbation method. The split Bregman algorithm is applied to solve the L1 norm regularization problem. The effectiveness and advantages of the proposed method are verified by numerical simulations. The average signal to noise ratio of the EMT system is 48 dB. Experimental results verified that the reconstructed images can show the number and positions of the imaging objects, demonstrating the feasibility and effectiveness of the novel imaging system design.
Electromagnetic tomography (EMT) is used to create tomographic images of the electrical properties of conducting material based on electromagnetic measurements from coils evenly distributed around the imaging region. EMT is widely used in industrial and biomedical fields for which it offers the advantages of being non-contact, fast, and non-radiative. Most EMT measurement systems are implemented with commercial instruments, such as impedance analyzers and lock-in amplifiers, which are bulky and inconvenient for portable detection devices. In order to improve the portability and extensibility, a purpose-built flexible and modularized EMT system is presented in this paper. The hardware system consists of six parts: the sensor array, signal conditioning module, lower computer module, data acquisition module, excitation signal module, and the upper computer. The complexity of the EMT system is reduced by a modularized design. The sensitivity matrix is calculated by the perturbation method. The split Bregman algorithm is applied to solve the L1 norm regularization problem. The effectiveness and advantages of the proposed method are verified by numerical simulations. The average signal to noise ratio of the EMT system is 48 dB. Experimental results verified that the reconstructed images can show the number and positions of the imaging objects, demonstrating the feasibility and effectiveness of the novel imaging system design.
Electromagnetic tomography (EMT) is used to create tomographic images of the electrical properties of conducting material based on electromagnetic measurements from coils evenly distributed around the imaging region. EMT is widely used in industrial and biomedical fields for which it offers the advantages of being non-contact, fast, and non-radiative. Most EMT measurement systems are implemented with commercial instruments, such as impedance analyzers and lock-in amplifiers, which are bulky and inconvenient for portable detection devices. In order to improve the portability and extensibility, a purpose-built flexible and modularized EMT system is presented in this paper. The hardware system consists of six parts: the sensor array, signal conditioning module, lower computer module, data acquisition module, excitation signal module, and the upper computer. The complexity of the EMT system is reduced by a modularized design. The sensitivity matrix is calculated by the perturbation method. The split Bregman algorithm is applied to solve the L norm regularization problem. The effectiveness and advantages of the proposed method are verified by numerical simulations. The average signal to noise ratio of the EMT system is 48 dB. Experimental results verified that the reconstructed images can show the number and positions of the imaging objects, demonstrating the feasibility and effectiveness of the novel imaging system design.
Electromagnetic tomography (EMT) is used to create tomographic images of the electrical properties of conducting material based on electromagnetic measurements from coils evenly distributed around the imaging region. EMT is widely used in industrial and biomedical fields for which it offers the advantages of being non-contact, fast, and non-radiative. Most EMT measurement systems are implemented with commercial instruments, such as impedance analyzers and lock-in amplifiers, which are bulky and inconvenient for portable detection devices. In order to improve the portability and extensibility, a purpose-built flexible and modularized EMT system is presented in this paper. The hardware system consists of six parts: the sensor array, signal conditioning module, lower computer module, data acquisition module, excitation signal module, and the upper computer. The complexity of the EMT system is reduced by a modularized design. The sensitivity matrix is calculated by the perturbation method. The split Bregman algorithm is applied to solve the L1 norm regularization problem. The effectiveness and advantages of the proposed method are verified by numerical simulations. The average signal to noise ratio of the EMT system is 48 dB. Experimental results verified that the reconstructed images can show the number and positions of the imaging objects, demonstrating the feasibility and effectiveness of the novel imaging system design.Electromagnetic tomography (EMT) is used to create tomographic images of the electrical properties of conducting material based on electromagnetic measurements from coils evenly distributed around the imaging region. EMT is widely used in industrial and biomedical fields for which it offers the advantages of being non-contact, fast, and non-radiative. Most EMT measurement systems are implemented with commercial instruments, such as impedance analyzers and lock-in amplifiers, which are bulky and inconvenient for portable detection devices. In order to improve the portability and extensibility, a purpose-built flexible and modularized EMT system is presented in this paper. The hardware system consists of six parts: the sensor array, signal conditioning module, lower computer module, data acquisition module, excitation signal module, and the upper computer. The complexity of the EMT system is reduced by a modularized design. The sensitivity matrix is calculated by the perturbation method. The split Bregman algorithm is applied to solve the L1 norm regularization problem. The effectiveness and advantages of the proposed method are verified by numerical simulations. The average signal to noise ratio of the EMT system is 48 dB. Experimental results verified that the reconstructed images can show the number and positions of the imaging objects, demonstrating the feasibility and effectiveness of the novel imaging system design.
Author Li, Danyang
Wang, Ying
Liu, Xianglong
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  article-title: Simulation research of impact of number of coils in EMT sensors on reconstructed images quality
  publication-title: Sens. Imaging
  doi: 10.1007/s11220-019-0250-2
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Snippet Electromagnetic tomography (EMT) is used to create tomographic images of the electrical properties of conducting material based on electromagnetic measurements...
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SubjectTerms Algorithms
Analyzers
Biomedical materials
Data acquisition
Effectiveness
Electric contacts
Electrical properties
Electromagnetic measurement
Image reconstruction
Lock in amplifiers
Modular design
Modules
Perturbation methods
Portable equipment
Regularization
Scientific apparatus & instruments
Sensor arrays
Signal to noise ratio
Systems design
Tomography
Title Imaging system design based on electromagnetic tomography for high conductivity medium reconstruction
URI http://dx.doi.org/10.1063/5.0126458
https://www.ncbi.nlm.nih.gov/pubmed/37012813
https://www.proquest.com/docview/2786621532
https://www.proquest.com/docview/2795362738
Volume 94
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