A New Divide-and-Conquer Method for 3-D Electrical Impedance Tomography.
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| Title: | A New Divide-and-Conquer Method for 3-D Electrical Impedance Tomography. |
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| Authors: | Martin, Sebastien1, Choi, Charles T. M.2 |
| Source: | IEEE Transactions on Magnetics. Mar2018, Vol. 54 Issue 3, p1-4. 4p. |
| Subjects: | Electrical impedance tomography, Inverse problems, Image reconstruction, Artificial neural networks, Electronic linearization |
| Abstract: | Electrical impedance tomography (EIT) is a patient-safe approach for imaging applications and is a promising technology for biomedical imaging. By applying a small electrical current to living tissue and measuring the electrical potential at different points of the tissue’s boundary, it is possible to solve the inverse problem and to generate a map of the tissue’s conductivities. Although it is a promising method, the reconstruction process remains a complicated task, and real-time nonlinear image reconstruction algorithms have not yet been widely adopted. Recent studies have proposed the use of artificial neural networks (ANN) to solve the EIT inverse problem. ANNs can give a nonlinear conductivity distribution. Although several studies have examined 2-D finite-element models, very little work has been done on 3-D problems. In this paper, a solution based on the divide-and-conquer method and ANNs is used to solve the nonlinear inverse problem without any linearization. The solution presented here reduces the difficulty of training large ANNs, which are commonly required to solve a 3-D EIT problem with artificial intelligence algorithms. [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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| Header | DbId: egs DbLabel: Engineering Source An: 128192060 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A New Divide-and-Conquer Method for 3-D Electrical Impedance Tomography. – 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>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Magnetics%22">IEEE Transactions on Magnetics</searchLink>. Mar2018, Vol. 54 Issue 3, p1-4. 4p. – Name: Subject Label: Subjects Group: Su Data: <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="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+linearization%22">Electronic linearization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Electrical impedance tomography (EIT) is a patient-safe approach for imaging applications and is a promising technology for biomedical imaging. By applying a small electrical current to living tissue and measuring the electrical potential at different points of the tissue’s boundary, it is possible to solve the inverse problem and to generate a map of the tissue’s conductivities. Although it is a promising method, the reconstruction process remains a complicated task, and real-time nonlinear image reconstruction algorithms have not yet been widely adopted. Recent studies have proposed the use of artificial neural networks (ANN) to solve the EIT inverse problem. ANNs can give a nonlinear conductivity distribution. Although several studies have examined 2-D finite-element models, very little work has been done on 3-D problems. In this paper, a solution based on the divide-and-conquer method and ANNs is used to solve the nonlinear inverse problem without any linearization. The solution presented here reduces the difficulty of training large ANNs, which are commonly required to solve a 3-D EIT problem with artificial intelligence algorithms. [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.2017.2750739 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 4 StartPage: 1 Subjects: – SubjectFull: Electrical impedance tomography Type: general – SubjectFull: Inverse problems Type: general – SubjectFull: Image reconstruction Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Electronic linearization Type: general Titles: – TitleFull: A New Divide-and-Conquer Method for 3-D Electrical Impedance Tomography. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Martin, Sebastien – PersonEntity: Name: NameFull: Choi, Charles T. M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 00189464 Numbering: – Type: volume Value: 54 – Type: issue Value: 3 Titles: – TitleFull: IEEE Transactions on Magnetics Type: main |
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