Adaptive techniques in electrical impedance tomography reconstruction

Li, Taoran; Isaacson, David; Newell, J.C.; Saulnier, G.J.

doi:10.1088/0967-3334/35/6/1111

Cited by 17 publications

(16 citation statements)

References 17 publications

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“…After that, it took inversion much longer to resolve smaller scale features of the object. Generally, these observations are in a very good agreement with other related studies (Haber et al 2007;Bangerth 2008;Beilina et al 2014;Li et al 2014).…”

Section: Adaptive Mesh Refinementsupporting

confidence: 93%

“…Nevertheless, many authors addressed this problem, showing that the idea of adaptive mesh refinement in inverse problems is feasible. In medical imaging, Bangerth (2008) and Li et al (2014) used adaptive finite-element method (FEM) to substantially reduce computational time and dimensionality of the problem. Similarly, Beilina et al (2014) used a rigorously elaborated method for adaptive mesh refinement in inverse problems arising from time-domain wave scattering.…”

mentioning

confidence: 99%

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Parallel three-dimensional magnetotelluric inversion using adaptive finite-element method. Part I: theory and synthetic study

Grayver

2015

Geophysical Journal International

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This paper presents a distributed magnetotelluric inversion scheme based on adaptive finiteelement method (FEM). The key novel aspect of the introduced algorithm is the use of automatic mesh refinement techniques for both forward and inverse modelling. These techniques alleviate tedious and subjective procedure of choosing a suitable model parametrization. To avoid overparametrization, meshes for forward and inverse problems were decoupled. For calculation of accurate electromagnetic (EM) responses, automatic mesh refinement algorithm based on a goal-oriented error estimator has been adopted. For further efficiency gain, EM fields for each frequency were calculated using independent meshes in order to account for substantially different spatial behaviour of the fields over a wide range of frequencies. An automatic approach for efficient initial mesh design in inverse problems based on linearized model resolution matrix was developed. To make this algorithm suitable for large-scale problems, it was proposed to use a low-rank approximation of the linearized model resolution matrix. In order to fill a gap between initial and true model complexities and resolve emerging 3-D structures better, an algorithm for adaptive inverse mesh refinement was derived. Within this algorithm, spatial variations of the imaged parameter are calculated and mesh is refined in the neighborhoods of points with the largest variations. A series of numerical tests were performed to demonstrate the utility of the presented algorithms. Adaptive mesh refinement based on the model resolution estimates provides an efficient tool to derive initial meshes which account for arbitrary survey layouts, data types, frequency content and measurement uncertainties. Furthermore, the algorithm is capable to deliver meshes suitable to resolve features on multiple scales while keeping number of unknowns low. However, such meshes exhibit dependency on an initial model guess. Additionally, it is demonstrated that the adaptive mesh refinement can be particularly efficient in resolving complex shapes. The implemented inversion scheme was able to resolve a hemisphere object with sufficient resolution starting from a coarse discretization and refining mesh adaptively in a fully automatic process. The code is able to harness the computational power of modern distributed platforms and is shown to work with models consisting of millions of degrees of freedom. Significant computational savings were achieved by using locally refined decoupled meshes.

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Section: Adaptive Mesh Refinementsupporting

confidence: 93%

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confidence: 99%

Parallel three-dimensional magnetotelluric inversion using adaptive finite-element method. Part I: theory and synthetic study

Grayver

2015

Geophysical Journal International

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“…Electrical impedance tomography (EIT) is a relatively new imaging technique [ 1 , 2 ]. Developed to overcome some of the disadvantages of existing imaging techniques, it provides flexibility, radiation-free operation, and noninvasive imaging ability and is relatively inexpensive [ 3 – 13 ]. The EIT system operates using low-power signal injection and measurements to reconstruct the estimated cross-section image [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…It works by detecting and tracking the electrical conductivity changes hidden in a medium such as a pipeline, human body parts, or chemical process [ 4 , 14 – 19 ]. The changes in conductivity are measured using several electrodes placed around the body [ 5 , 10 , 13 , 20 – 22 ]. Numerous EIT researchers have studied and attempted to implement a system that can estimate the hidden object structure (shape), which may help to identify the hidden object [ 4 , 13 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…The changes in conductivity are measured using several electrodes placed around the body [ 5 , 10 , 13 , 20 – 22 ]. Numerous EIT researchers have studied and attempted to implement a system that can estimate the hidden object structure (shape), which may help to identify the hidden object [ 4 , 13 , 23 , 24 ]. Their systems have functioned by controlling the injection and measurement signal parameters such as injection frequency, voltage amplitude, and phase [ 1 , 8 , 9 , 11 , 14 , 16 , 25 – 27 ].…”

Section: Introductionmentioning

confidence: 99%

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A Multitasking Electrical Impedance Tomography System Using Titanium Alloy Electrode

Salama

Malekmohammadi

Mohanna

et al. 2017

International Journal of Biomedical Imaging

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This paper presents a multitasking electrical impedance tomography (EIT) system designed to improve the flexibility and durability of an existing EIT system. The ability of the present EIT system to detect, locate, and reshape objects was evaluated by four different experiments. The results of the study show that the system can detect and locate an object with a diameter as small as 1.5 mm in a testing tank with a diameter of 134 mm. Moreover, the results demonstrate the ability of the current system to reconstruct an image of several dielectric object shapes. Based on the results of the experiments, the programmable EIT system can adapt the EIT system for different applications without the need to implement a new EIT system, which may help to save time and cost. The setup for all the experiments consisted of a testing tank with an attached 16-electrode array made of titanium alloy grade 2. The titanium alloy electrode was used to enhance EIT system's durability and lifespan.

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Electrical impedance tomography image reconstruction based on backprojection and extreme learning machines

et al. 2020

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Purpose Electrical impedance tomography (EIT) is an image technique based on the application of an alternating electrical current on electrodes placed on the surface of the domain, which are also responsible for measuring the resulting electrical potentials. EIT main advantages are portability, low cost, and nonuse of ionizing radiation. EIT image reconstruction depends on the resolution of the direct and inverse problems, which is nonlinear and ill-posed. Several reconstruction methods have been used to solve EIT inverse problem, from Newton-based methods to bio-and social-inspired metaheuristics. Method In this work, we propose a new approach: the use of random-weighted neural networks, specifically extreme learning machines (ELM), to approximate sinograms from electrical potential data and, therefore, use the classical backprojection algorithm for image reconstruction. We generated a database of 4000 synthetic 128 × 128 images. We trained 16 ELMs corresponding to a 16-electrode EIT system placed on a circular domain. Results Results were evaluated according to peak-to-noise ratio (PSNR) and Structural Similarity Index (SSIM), as well as visual inspection. Results are fair and similar to image reconstructions obtained by the direct application of the backprojection algorithm, in case this reconstruction problem would be a classic tomographic reconstruction. Conclusion Our work suggested the use of ELMs to create sinograms from the electrical potential data of the studied domain. Sinograms could be easily reconstructed by the backprojection technique for final image generation, where objects and domain are represented. Finally, we were able to reduce the time of EIT reconstruction, mainly due to the high speed of the ELMs, and obtain reasonable and consistent images.

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Adaptive techniques in electrical impedance tomography reconstruction

Cited by 17 publications

References 17 publications

Parallel three-dimensional magnetotelluric inversion using adaptive finite-element method. Part I: theory and synthetic study

Parallel three-dimensional magnetotelluric inversion using adaptive finite-element method. Part I: theory and synthetic study

A Multitasking Electrical Impedance Tomography System Using Titanium Alloy Electrode

Electrical impedance tomography image reconstruction based on backprojection and extreme learning machines

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