ROI-Based Image Reconstruction of Electrical Impedance Tomography Used to Detect Regional Conductivity Variation

Miao, Liwen; Ma, Yunlong; Wang, Junpu

doi:10.1109/tim.2014.2326765

Cited by 31 publications

(12 citation statements)

References 22 publications

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“…This process was performed for all 16 pairs, generating 256 measurements for image reconstruction. Although only 104 of these measurements were independent, it is common practice to keep all of the measurements to reduce the influence of noise [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

A novel post-processing scheme for two-dimensional electrical impedance tomography based on artificial neural networks

Martin

2017

PLoS ONE

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ObjectiveElectrical Impedance Tomography (EIT) is a powerful non-invasive technique for imaging applications. The goal is to estimate the electrical properties of living tissues by measuring the potential at the boundary of the domain. Being safe with respect to patient health, non-invasive, and having no known hazards, EIT is an attractive and promising technology. However, it suffers from a particular technical difficulty, which consists of solving a nonlinear inverse problem in real time. Several nonlinear approaches have been proposed as a replacement for the linear solver, but in practice very few are capable of stable, high-quality, and real-time EIT imaging because of their very low robustness to errors and inaccurate modeling, or because they require considerable computational effort.MethodsIn this paper, a post-processing technique based on an artificial neural network (ANN) is proposed to obtain a nonlinear solution to the inverse problem, starting from a linear solution. While common reconstruction methods based on ANNs estimate the solution directly from the measured data, the method proposed here enhances the solution obtained from a linear solver.ConclusionApplying a linear reconstruction algorithm before applying an ANN reduces the effects of noise and modeling errors. Hence, this approach significantly reduces the error associated with solving 2D inverse problems using machine-learning-based algorithms.SignificanceThis work presents radical enhancements in the stability of nonlinear methods for biomedical EIT applications.

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Section: Methodsmentioning

confidence: 99%

A novel post-processing scheme for two-dimensional electrical impedance tomography based on artificial neural networks

Martin

2017

PLoS ONE

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“…Alternatively, subdomain methods and similar approaches rely on prior information to limit the reconstruction to specific regions of the domain [18]. While these methods are generally application-specific, they are associated with increased accuracy and a reduced reconstruction time [17,26,32]. Several branches of artificial intelligence, such as neural networks, swarm intelligence, and evolutionary methods have been applied to the EIT image reconstruction problem.…”

Section: Related Workmentioning

confidence: 99%

Spatially-Adaptive Variational Reconstructions for Linear Inverse Electrical Impedance Tomography

et al. 2020

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The inverse electrical impedance tomography (EIT) problem involves collecting electrical measurements on the smooth boundary of a region to determine the spatially varying electrical conductivity distribution within the bounded region. Effective applications of EIT technology emerged in different areas of engineering, technology, and applied sciences. However, the mathematical formulation of EIT is well known to suffer from a high degree of nonlinearity and severe ill-posedness. Therefore, regularization is required to produce reasonable electrical impedance images. Using difference imaging, we propose a spatially-variant variational method which couples sparsity regularization and smoothness regularization for improved EIT linear reconstructions. The EIT variational model can benefit from structural prior information in the form of an edge detection map coming either from an auxiliary image of the same object being reconstructed or automatically detected. We propose an efficient algorithm for minimizing the (non-convex) function based on the alternating direction method of multipliers. Experiments are presented which strongly indicate that using non-convex versus convex variational EIT models holds the potential for more accurate reconstructions.

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“…The work (Miao et al 2014) proposed a strategy to limit the conductivity spectrum to a region of interest (ROI), creating an adapted method from the linear sensitivity-theorem-based conjugate gradient (SCG), namely, RROI. The idea is to use all boundary informations to reconstruct image within a specific ROI, considering that for some applications, the conductivity varies only in a determined region of the domain.…”

Section: Related Workmentioning

confidence: 99%

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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ROI-Based Image Reconstruction of Electrical Impedance Tomography Used to Detect Regional Conductivity Variation

Cited by 31 publications

References 22 publications

A novel post-processing scheme for two-dimensional electrical impedance tomography based on artificial neural networks

A novel post-processing scheme for two-dimensional electrical impedance tomography based on artificial neural networks

Spatially-Adaptive Variational Reconstructions for Linear Inverse Electrical Impedance Tomography

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

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