Abstract
Electrical impedance tomography views the electrical properties of the objects by injecting current with surface electrodes and measuring voltages. Then using a reconstructing algorithm, from the measured voltage-current values, conductivity distribution of the object calculated. Finding internal conductivity from surface voltage-current measurements is a reverse and ill-posed problem. Therefore, high error sensitivity, and making approximations in conceiving complex computations cause to limited spatial resolution. The classic iterative image reconstruction algorithms have reconstruction errors. Accordingly, Electrical impedance tomography images suffer low accuracy. It is necessary to evaluate the collected data from the object surface with a new approach. In this paper, the forward problem solved with the finite element method to reconstruct the conductivity distribution inside the object, the reverse problem solved by the neural network approach. Image reconstruction speed, conceptual simplicity, and ease of implementation maintained by this approach.
Highlights
Electrical impedance tomography (EIT) creates the object’s internal electrical conductivity or resistivity by injecting current with electrodes on the surface, and the corresponding voltages are measured with electrodes using the neighboring method
The most common non-linear method is Newton–Raphson. This technique works by searching the conductivity distribution that reduces the difference between applied potentials and the calculated potential from the forward problem by the finite element method
In the electrical impedance image reconstruction process, it is necessary for a new approach to evaluate the data measured from the surface of the object
Summary
Electrical impedance tomography (EIT) creates the object’s internal electrical conductivity or resistivity by injecting current with electrodes on the surface, and the corresponding voltages are measured with electrodes using the neighboring method. The most common non-linear method is Newton–Raphson This technique works by searching the conductivity distribution that reduces the difference between applied potentials and the calculated potential from the forward problem by the finite element method. The drawback of this algorithm is that it is computationally complex, requires numerous orders of magnitude and computational time is high as compared to the linear methods [3]. For Improving spatial resolution, electrode numbers can be increased, but implementing a lot of electrodes can not be possible in most real situations [7]
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