scholarly journals Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 88 ◽  
Author(s):  
Talha Ali Khan ◽  
Sai Ho Ling
Keyword(s):  
Artificial Intelligence ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Imaging Techniques ◽  
Imaging Method ◽  
Impedance Tomography ◽  
Ill Posed ◽  
Magnetic Resonance Imaging Mri ◽  
Electrical Properties Of Tissues

Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented.

Patch-based sparse reconstruction for electrical impedance tomography

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 257-269 ◽  
Author(s):  
Qi Wang ◽  
Pengcheng Zhang ◽  
Jianming Wang ◽  
Qingliang Chen ◽  
Zhijie Lian ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Sparse Representation ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Inverse Operator ◽  
Reconstruction Algorithm ◽  
Impedance Tomography ◽  
Content Type ◽  
Ill Posed ◽  
High Level

Purpose Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise. Design/methodology/approach This paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary optimization and image reconstruction are performed alternately. Two patch-based sparsity, namely, square-patch sparsity and column-patch sparsity, are discussed and compared with the global sparsity. Findings Both simulation and experimental results indicate that the patch based sparsity method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages. Originality/value EIT image is reconstructed based on patch-based sparse representation. Square-patch sparsity and column-patch sparsity are proposed and compared. Sparse dictionary optimization and image reconstruction are performed alternately. The new method tolerates a relatively high level of noise in measured voltages.

Using Extreme Learning Machines and the Backprojection Algorithm as an Alternative to Reconstruct Electrical Impedance Tomography Images

2021 ◽  
pp. 16-27
Author(s):  
Juliana Carneiro Gomes ◽  
Maíra Araújo de Santana ◽  
Clarisse Lins de Lima ◽  
Ricardo Emmanuel de Souza ◽  
Wellington Pinheiro dos Santos
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Imaging Techniques ◽  
Low Cost ◽  
Computational Cost ◽  
Extreme Learning Machines ◽  
Impedance Tomography ◽  
Learning Machines ◽  
Electrical Potentials ◽  
Reconstruction Methods

Electrical Impedance Tomography (EIT) is an imaging technique based on the excitation of electrode pairs applied to the surface of the imaged region. The electrical potentials generated from alternating current excitation are measured and then applied to boundary-based reconstruction methods. When compared to other imaging techniques, EIT is considered a low-cost technique without ionizing radiation emission, safer for patients. However, the resolution is still low, depending on efficient reconstruction methods and low computational cost. EIT has the potential to be used as an alternative test for early detection of breast lesions in general. The most accurate reconstruction methods tend to be very costly as they use optimization methods as a support. Backprojection tends to be rapid but more inaccurate. In this work, the authors propose a hybrid method, based on extreme learning machines and backprojection for EIT reconstruction. The results were applied to numerical phantoms and were considered adequate, with potential to be improved using post processing techniques.

scholarly journals A Krylov subspace type method for Electrical Impedance Tomography

2021 ◽  
Author(s):  
Mirjeta Pasha ◽  
Shyla Kupis ◽  
Sanwar Ahmad ◽  
Taufiquar Khan
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Krylov Subspace ◽  
Low Frequency ◽  
Small Dimension ◽  
Impedance Tomography ◽  
Type Method ◽  
Newton Algorithm ◽  
Ill Posed ◽  
Internal Conductivity

Electrical Impedance Tomography (EIT) is a well-known imaging technique for detecting the electrical properties of an object in order to detect anomalies, such as conductive or resistive targets. More specifically, EIT has many applications in medical imaging for the detection and location of bodily tumors since it is an affordable and non-invasive method, which aims to recover the internal conductivity of a body using voltage measurements resulting from applying low frequency current at electrodes placed at its surface. Mathematically, the reconstruction of the internal conductivity is a severely ill-posed inverse problem and yields a poor quality image reconstruction. To remedy this difficulty, at least in  part, we regularize and solve the nonlinear minimization problem by the aid of a Krylov subspace-type method for the linear sub problem during each iteration.  In EIT, a tumor or general anomaly can be modeled as a piecewise constant perturbation of a smooth background, hence, we solve the regularized problem on a subspace of relatively small dimension by the Flexible Golub-Kahan process that provides solutions that have sparse representation. For comparison, we use a well-known modified Gauss-Newton algorithm as a benchmark. Using simulations, we demonstrate the effectiveness of the proposed method. The obtained reconstructions indicate that the Krylov subspace method is better adapted to solve the ill-posed EIT problem and results in higher resolution images and faster convergence compared to reconstructions using the modified Gauss-Newton algorithm.

scholarly journals Reconstruction of Electrical Impedance Tomography Using Fish School Search, Non-Blind Search, and Genetic Algorithm

2017 ◽  
Vol 8 (2) ◽  
pp. 17-33 ◽  
Author(s):  
Valter A. F. Barbosa ◽  
Reiga R. Ribeiro ◽  
Allan R. S. Feitosa ◽  
Victor L. B. A. Silva ◽  
Arthur D. D. Rocha ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Imaging Technique ◽  
Optimization Method ◽  
Impedance Tomography ◽  
Circular Section ◽  
Fish School ◽  
Noninvasive Imaging Technique ◽  
Ill Posed ◽  
Blind Search

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that does not use ionizing radiation, with application both in environmental sciences and in health. Image reconstruction is performed by solving an inverse problem and ill-posed. Evolutionary Computation and Swarm Intelligence have become a source of methods for solving inverse problems. Fish School Search (FSS) is a promising search and optimization method, based on the dynamics of schools of fish. In this article the authors present a method for reconstruction of EIT images based on FSS and Non-Blind Search (NBS). The method was evaluated using numerical phantoms consisting of electrical conductivity images with subjects in the center, between the center and the edge and on the edge of a circular section, with meshes of 415 finite elements. The authors performed 20 simulations for each configuration. Results showed that both FSS and FSS-NBS were able to converge faster than genetic algorithms.

Deep Autoencoder Imaging Method for Electrical Impedance Tomography

2021 ◽  
pp. 1-1
Author(s):  
Xiaoyan Chen ◽  
Zichen Wang ◽  
Xinyu Zhang ◽  
Rong Fu ◽  
Di Wanga ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Imaging Method ◽  
Impedance Tomography

scholarly journals Image Reconstruction Based on Homotopy Perturbation Inversion Method for Electrical Impedance Tomography

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jing Wang ◽  
Bo Han
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Perturbation Technique ◽  
Inversion Method ◽  
Impedance Tomography ◽  
Data Noise ◽  
Homotopy Perturbation ◽  
Ill Posed ◽  
Homotopy Perturbation Technique

The image reconstruction for electrical impedance tomography (EIT) mathematically is a typed nonlinear ill-posed inverse problem. In this paper, a novel iteration regularization scheme based on the homotopy perturbation technique, namely, homotopy perturbation inversion method, is applied to investigate the EIT image reconstruction problem. To verify the feasibility and effectiveness, simulations of image reconstruction have been performed in terms of considering different locations, sizes, and numbers of the inclusions, as well as robustness to data noise. Numerical results indicate that this method can overcome the numerical instability and is robust to data noise in the EIT image reconstruction. Moreover, compared with the classical Landweber iteration method, our approach improves the convergence rate. The results are promising.

scholarly journals Use of artificial intelligence and neural networks for analysis and gesture detection in electrical impedance tomography

2020 ◽  
Vol 6 (3) ◽  
pp. 489-492
Author(s):  
Christian Gibas ◽  
Luca Mülln ◽  
Rainer Brück
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Precise Analysis ◽  
User Friendly ◽  
And Control

AbstractArtificial intelligence and neural networks are getting more and more relevant for several types of application. The field of prosthesis technology currently uses electromyography for controllable prosthesis. The precision of the control suffers from the use of EMG. More precise and more collected data with the help of EIT allows a much more precise analysis and control of the prosthesis. In this paper a neural network for gesture detection using EIT is developed and presented in a user-friendly way.

Sign in / Sign up

Export Citation Format

Share Document