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  • Author: Nagi Farrukh x
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Spatial resolution in electrical impedance tomography: A topical review

Abstract

Electrical impedance tomography (EIT) is a relatively new imaging technique. It has the advantages of low cost, portability, non-invasiveness and is free from radiation effects. So far, this imaging technique has shown satisfactory results in functional imaging. However, it is not yet fully suitable for anatomical imaging due to its poor spatial resolution. In this paper, we review the basic directions of research in the area of the spatial resolution of the EIT systems. The improvements to the hardware and the software developments are highlighted. Finally, possible techniques to enhance the spatial resolution of the EIT systems using array processing beamforming methods are discussed.

Open access
Narrowband array processing beamforming technique for electrical impedance tomography

Abstract

Electrical impedance tomography (EIT) has a large potential as a two dimensional imaging technique and is gaining attention among researchers across various fields of engineering. Beamforming techniques stem from the array signal processing field and is used for spatial filtering of array data to evaluate the location of objects. In this work the circular electrodes are treated as an array of sensors and beamforming technique is used to localize the object(s) in an electrical field. The conductivity distributions within a test tank is obtained by an EIT system in terms of electrode voltages. These voltages are then interpolated using elliptic partial differential equations. Finally, a narrowband beamformer detects the peak in the output response signal to localize the test object(s). Test results show that the beamforming technique can be used as a secondary method that may provide complementary information about accurate position of the test object(s) using an eight electrode EIT system. This method could possibly open new avenues for spatial EIT data filtering techniques with an understanding that the inverse problem is more likely considered here as a source localization algorithm instead as an image reconstruction algorithm.

Open access