2012
DOI: 10.1088/0266-5611/28/9/095010
|View full text |Cite
|
Sign up to set email alerts
|

Hybrid topological derivative and gradient-based methods for electrical impedance tomography

Abstract: We present a technique to reconstruct the electromagnetic properties of a medium or a set of objects buried inside it from boundary measurements when applying electric currents through a set of electrodes. The electromagnetic parameters may be recovered by means of a gradient method without a priori information on the background. The shape, location and size of objects, when present, are determined by a topological derivative-based iterative procedure. The combination of both strategies allows improved reconst… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
54
0

Year Published

2013
2013
2022
2022

Publication Types

Select...
7
1

Relationship

0
8

Authors

Journals

citations
Cited by 30 publications
(54 citation statements)
references
References 38 publications
0
54
0
Order By: Relevance
“…We assume here that E is known at the detectors. Topological derivative based methods have been implemented in many inverse scattering problems, see [6,8,10,23,28,29] for applications in acoustics, elasticity, fluids and tomography, for instance, usually in two-dimensional settings. They have the ability of providing first guesses of objects in absence of any a priori information.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…We assume here that E is known at the detectors. Topological derivative based methods have been implemented in many inverse scattering problems, see [6,8,10,23,28,29] for applications in acoustics, elasticity, fluids and tomography, for instance, usually in two-dimensional settings. They have the ability of providing first guesses of objects in absence of any a priori information.…”
Section: Introductionmentioning
confidence: 99%
“…Both are often initialized using topological derivatives. If the material parameters of the anomalies are unknown, we may combine these techniques with gradient methods to approximate the spatial variation of such parameters within the objects [7,8]. Alternatively, we may apply from the beginning strategies to reconstruct the parameter profile distribution everywhere.…”
Section: Introductionmentioning
confidence: 99%
“…In a few recent studies [15,[52][53][54] dealing with acoustic and electrical impedance tomography problems, the topological sensitivity has been effectively employed to iteratively update the geometric approximations of hidden/buried obstacles. By analogy to these developments, an iterative 3D obstacle reconstruction approach based on the topological sensitivity established for acoustic scattering of solid obstacles is established and verified in the following analysis.…”
Section: Iterative 3d Obstacle Reconstructionmentioning
confidence: 99%
“…The 3D distribution of topological sensitivity, calculated using proper TS formulas, is used to iteratively update the geometry of hidden obstacles. The procedures, similar to those in [15,[52][53][54], are described in the following.…”
Section: Iterative Algorithmmentioning
confidence: 99%
“…Many promising computational and mathematical frameworks adaptive to different imaging and experimental setups have been developed to address these inverse problems over a span of last few decades (see, e.g., [6][7][8][9][10][11][12][13][14][15][16]). In particular, topological sensitivity frameworks have received significant attention for the reconstruction of location, shape or constitutive parameters of anomalies due to their simplicity and robustness (see, e.g., [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]).…”
Section: Introductionmentioning
confidence: 99%