Iterative reconstruction methods using regularization and optimal current patterns in electrical impedance tomography

1991 ◽  
Vol 10 (4) ◽  
pp. 621-628 ◽  
Author(s):  
P. Hua ◽  
E.J. Woo ◽  
J.G. Webster ◽  
W.J. Tompkins
Keyword(s):  
Electrical Impedance Tomography ◽  
Iterative Reconstruction ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Reconstruction Methods ◽  
Optimal Current

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.

On Optimal Current Patterns for Electrical Impedance Tomography

2005 ◽  
Vol 52 (2) ◽  
pp. 238-248 ◽  
Author(s):  
E. Demidenko ◽  
A. Hartov ◽  
N. Soni ◽  
K.D. Paulsen
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Optimal Current

Optimal current patterns in dynamical electrical impedance tomography imaging

2007 ◽  
Vol 23 (3) ◽  
pp. 1201-1214 ◽  
Author(s):  
Jari P Kaipio ◽  
Aku Seppänen ◽  
Arto Voutilainen ◽  
Heikki Haario
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Tomography Imaging ◽  
Optimal Current

Posterior covariance related optimal current patterns in electrical impedance tomography

2004 ◽  
Vol 20 (3) ◽  
pp. 919-936 ◽  
Author(s):  
J P Kaipio ◽  
A Seppänen ◽  
E Somersalo ◽  
H Haario
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Optimal Current

scholarly journals Survey on Medical Imaging of Electrical Impedance Tomography (EIT) by Variable Current Pattern Methods

2021 ◽  
Vol 2 (2) ◽  
pp. 82-95
Author(s):  
Edriss Eisa Babikir Adam ◽  
Sathesh
Keyword(s):  
Image Reconstruction ◽  
Medical Imaging ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Optical Tomography ◽  
Assessment Process ◽  
Current Direction ◽  
Impedance Tomography ◽  
Research Article ◽  
Reconstruction Methods

Recently, the image reconstruction study on EIT plays a vital role in the medical application field for validation and calibration purpose. This research article analyzes the different types of reconstruction algorithms of EIT in medical imaging applications. Besides, it reviews many methods involved in constructing the electrical impedance tomography. The spatial distribution and resolution with different sensitivity has been discussed here. The electrode arrangement of various methods involved in the EIT system is discussed here. This research article comprises of adjacent drive method, cross method, and alternative opposite current direction method based on the voltage driven pattern. The assessment process of biomedical EIT has been discussed and investigated through the impedance imaging of the existent substances. The locality of the electrodes can be calculated and fixed for appropriate methods. More specifically, this research article discusses about the EIT image reconstruction methods and the significance of the alternative opposite current direction approach in the biomedical system. The change in conductivity test is further investigated based on the injection of current flow in the system. It has been established by the use of Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EDITORS) software, which is open-source software.

scholarly journals Electrical Impedance tomography – recent applications and developments

2021 ◽  
Vol 12 (1) ◽  
pp. 50-62
Author(s):  
Sofiene Mansouri ◽  
Yousef Alharbi ◽  
Fatma Haddad ◽  
Souhir Chabcoub ◽  
Anwar Alshrouf ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Low Cost ◽  
Noninvasive Imaging ◽  
Imaging Method ◽  
Impedance Tomography ◽  
Large Margin ◽  
Reconstruction Methods ◽  
Imaging Approach

Abstract Electrical impedance tomography (EIT) is a low-cost noninvasive imaging method. The main purpose of this paper is to highlight the main aspects of the EIT method and to review the recent advances and developments. The advances in instrumentation and in the different image reconstruction methods and systems are demonstrated in this review. The main applications of the EIT are presented and a special attention made to the papers published during the last years (from 2015 until 2020). The advantages and limitations of EIT are also presented. In conclusion, EIT is a promising imaging approach with a strong potential that has a large margin of progression before reaching the maturity phase.

scholarly journals The Factorization Method for Electrical Impedance Tomography Data from a New Planar Device

2007 ◽  
Vol 2007 ◽  
pp. 1-7 ◽  
Author(s):  
Mustapha Azzouz ◽  
Martin Hanke ◽  
Chantal Oesterlein ◽  
Karl Schilcher
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Imaging Techniques ◽  
Factorization Method ◽  
The Body ◽  
Impedance Tomography ◽  
Type Method ◽  
Reconstruction Methods ◽  
Tomography Data ◽  
Electrical Impedance Tomography Data

We present numerical results for two reconstruction methods for a new planar electrical impedance tomography device. This prototype allows noninvasive medical imaging techniques if only one side of a patient is accessible for electric measurements. The two reconstruction methods have different properties: one is a linearization-type method that allows quantitative reconstructions; the other one, that is, the factorization method, is a qualitative one, and is designed to detect anomalies within the body.

scholarly journals Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Kyounghun Lee ◽  
Minha Yoo ◽  
Ariungerel Jargal ◽  
Hyeuknam Kwon
Keyword(s):  
Deep Learning ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Subcutaneous Fat ◽  
Estimation Method ◽  
Training Dataset ◽  
Strong Nonlinearity ◽  
Learning Method ◽  
Impedance Tomography ◽  
Reconstruction Methods

This paper proposes a deep learning method based on electrical impedance tomography (EIT) to estimate the thickness of abdominal subcutaneous fat. EIT for evaluating the thickness of abdominal subcutaneous fat is an absolute imaging problem that aims at reconstructing conductivity distributions from current-to-voltage data. Existing reconstruction methods based on EIT have difficulty handling the inherent drawbacks of strong nonlinearity and severe ill-posedness of EIT; hence, absolute imaging may not be possible using linearized methods. To handle nonlinearity and ill-posedness, we propose a deep learning method that finds useful solutions within a restricted admissible set by accounting for prior information regarding abdominal anatomy. We determined that a specially designed training dataset used during the deep learning process significantly reduces ill-posedness in the absolute EIT problem. In the preprocessing stage, we normalize current-voltage data to alleviate the effects of electrodeposition and body geometry by exploiting knowledge regarding electrode positions and body geometry. The performance of the proposed method is demonstrated through numerical simulations and phantom experiments using a 10 channel EIT system and a human-like domain.

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