The Bayesian approximation error approach for electrical impedance tomography—experimental results

2007 ◽  
Vol 19 (1) ◽  
pp. 015501 ◽  
Author(s):  
A Nissinen ◽  
L M Heikkinen ◽  
J P Kaipio
2004 ◽  
Vol 25 (1) ◽  
pp. 239-255 ◽  
Author(s):  
Sharon Zlochiver ◽  
M Michal Radai ◽  
Shimon Abboud ◽  
Moshe Rosenfeld ◽  
Xiu-Zhen Dong ◽  
...  

2014 ◽  
Vol 13 (4) ◽  
pp. 156-164
Author(s):  
Ye. S. Sherina ◽  
A. V. Starchenko

This research has been aimed to carry out a study of peculiarities that arise in a numerical simulation of the electrical impedance tomography (EIT) problem. Static EIT image reconstruction is sensitive to a measurement noise and approximation error. A special consideration has been given to reducing of the approximation error, which originates from numerical implementation drawbacks. This paper presents in detail two numerical approaches for solving EIT forward problem. The finite volume method (FVM) on unstructured triangular mesh is introduced. In order to compare this approach, the finite element (FEM) based forward solver was implemented, which has gained the most popularity among researchers. The calculated potential distribution with the assumed initial conductivity distribution has been compared to the analytical solution of a test Neumann boundary problem and to the results of problem simulation by means of ANSYS FLUENT commercial software. Two approaches to linearized EIT image reconstruction are discussed. Reconstruction of the conductivity distribution is an ill-posed problem, typically requiring a large amount of computation and resolved by minimization techniques. The objective function to be minimized is constructed of measured voltage and calculated boundary voltage on the electrodes. A classical modified Newton type iterative method and the stochastic differential evolution method are employed. A software package has been developed for the problem under investigation. Numerical tests were conducted on simulated data. The obtained results could be helpful to researches tackling the hardware and software issues for medical applications of EIT.


Author(s):  
Bruno Furtado de Moura ◽  
francisco sepulveda ◽  
Jorge Luis Jorge Acevedo ◽  
Wellington Betencurte da Silva ◽  
Rogerio Ramos ◽  
...  

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