Nonlinear Electrical Impedance Tomography Reconstruction Using Artificial Neural Networks and Particle Swarm Optimization.
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| Title: | Nonlinear Electrical Impedance Tomography Reconstruction Using Artificial Neural Networks and Particle Swarm Optimization. |
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| Authors: | Martin, Sebastien1, Choi, Charles T. M.1 |
| Source: | IEEE Transactions on Magnetics. Mar2016, Vol. 52 Issue 3, p1-4. 4p. |
| Subjects: | Nonlinear theories, Electrical impedance tomography, Inverse problems, Artificial neural networks, Particle swarm optimization, Stochastic convergence |
| Abstract: | Electrical impedance tomography (EIT) is an imaging technology that offers the advantages of being noninvasive, and it does not generate ionizing radiation. The main difficulty in applying EIT is to solve an ill-posed nonlinear inverse problem. Given a set of electrical voltages measured at the surface of a volume conductor, the goal is to identify the materials that are present in the domain by determining their electrical conductivities. However, since EIT is a nonlinear problem, various algorithms proposed in the literature can only approximate real conductivity distributions. Nonlinear algorithms, especially artificial neural networks (ANNs), have been proposed to solve this inverse problem, but these algorithms are usually limited by slow convergence issues during the training phase. In this paper, the particle swarm optimization (PSO) method is used to train an ANN to solve the EIT problem. It has been found that, compared with the back-propagation algorithm, PSO is capable of generating both faster and higher convergence. This paper also shows that the proposed method is capable of dealing with noisy data and the imperfections in the finite-element discretization, an important source of errors in EIT imaging. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Magnetics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Nonlinear Electrical Impedance Tomography Reconstruction Using Artificial Neural Networks and Particle Swarm Optimization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Martin%2C+Sebastien%22">Martin, Sebastien</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Choi%2C+Charles+T%2E+M%2E%22">Choi, Charles T. M.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Magnetics%22">IEEE Transactions on Magnetics</searchLink>. Mar2016, Vol. 52 Issue 3, p1-4. 4p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Nonlinear+theories%22">Nonlinear theories</searchLink><br /><searchLink fieldCode="DE" term="%22Electrical+impedance+tomography%22">Electrical impedance tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Inverse+problems%22">Inverse problems</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+convergence%22">Stochastic convergence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Electrical impedance tomography (EIT) is an imaging technology that offers the advantages of being noninvasive, and it does not generate ionizing radiation. The main difficulty in applying EIT is to solve an ill-posed nonlinear inverse problem. Given a set of electrical voltages measured at the surface of a volume conductor, the goal is to identify the materials that are present in the domain by determining their electrical conductivities. However, since EIT is a nonlinear problem, various algorithms proposed in the literature can only approximate real conductivity distributions. Nonlinear algorithms, especially artificial neural networks (ANNs), have been proposed to solve this inverse problem, but these algorithms are usually limited by slow convergence issues during the training phase. In this paper, the particle swarm optimization (PSO) method is used to train an ANN to solve the EIT problem. It has been found that, compared with the back-propagation algorithm, PSO is capable of generating both faster and higher convergence. This paper also shows that the proposed method is capable of dealing with noisy data and the imperfections in the finite-element discretization, an important source of errors in EIT imaging. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Magnetics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TMAG.2015.2488901 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 4 StartPage: 1 Subjects: – SubjectFull: Nonlinear theories Type: general – SubjectFull: Electrical impedance tomography Type: general – SubjectFull: Inverse problems Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Stochastic convergence Type: general Titles: – TitleFull: Nonlinear Electrical Impedance Tomography Reconstruction Using Artificial Neural Networks and Particle Swarm Optimization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Martin, Sebastien – PersonEntity: Name: NameFull: Choi, Charles T. M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 00189464 Numbering: – Type: volume Value: 52 – Type: issue Value: 3 Titles: – TitleFull: IEEE Transactions on Magnetics Type: main |
| ResultId | 1 |
