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Electrical Impedance tomography – recent applications and developments

Sofiene Mansouri

Sofiene Mansouri

  • Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz UniversityAl-Kharj, Saudi Arabia
  • Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El ManarTunis, Tunisia
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Mansouri, Sofiene
, 
Yousef Alharbi

Yousef Alharbi

Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz UniversityAl-Kharj, Saudi Arabia
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Alharbi, Yousef
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Fatma Haddad

Fatma Haddad

Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El ManarTunis, Tunisia
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Haddad, Fatma
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Souhir Chabcoub

Souhir Chabcoub

Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El ManarTunis, Tunisia
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Chabcoub, Souhir
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Anwar Alshrouf

Anwar Alshrouf

Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz UniversityAl-Kharj, Saudi Arabia
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Alshrouf, Anwar
 und 
Amr A. Abd-Elghany

Amr A. Abd-Elghany

  • Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz UniversityAl-Kharj, Saudi Arabia
  • Biophysics Department, Faculty of Science, Cairo UniversityCairo, Egypt
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Abd-Elghany, Amr A.
  
06. Okt. 2021
Electrical Impedance tomography – recent applications and developments's Cover Image
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Artikel-Kategorie: Review article
Online veröffentlicht: 06. Okt. 2021
Seitenbereich: 50 - 62
Eingereicht: 08. März 2021
DOI: https://doi.org/10.2478/joeb-2021-0007
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© 2021 Sofiene Mansouri, Yousef Alharbi, Fatma Haddad, Souhir Chabcoub, Anwar Alshrouf, and Amr A. Abd-Elghany, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig.1

The equivalent electrical circuit model for tissues.
The equivalent electrical circuit model for tissues.

Fig. 2

Main parts of an EIT imaging system.
Main parts of an EIT imaging system.

Fig. 3

Chest image reconstruction by EIT [21].
Chest image reconstruction by EIT [21].

Fig. 4

FEM reconstruction, Left: 2D mesh with 340 finite elements for 16 surface electrodes. Right: FEM-based reconstruction of the bioimpedance distribution.
FEM reconstruction, Left: 2D mesh with 340 finite elements for 16 surface electrodes. Right: FEM-based reconstruction of the bioimpedance distribution.

Fig. 5

Comparison of the phantom reconstruction obtained with the linear inverse solver (left), and the proposed method using ANN and PSO (right). Both targets are indicated by red circles [54].
Comparison of the phantom reconstruction obtained with the linear inverse solver (left), and the proposed method using ANN and PSO (right). Both targets are indicated by red circles [54].

Fig. 6

Series of dynamic images showing air filling during inspiration by the PulmoVista500 system [81].
Series of dynamic images showing air filling during inspiration by the PulmoVista500 system [81].

Fig. 7

Cardiac monitoring by CardioInspect system [88].
Cardiac monitoring by CardioInspect system [88].

Comparison between different imaging modalities_

Medical imaging modality CT US MRI EIT
Basic principle X-rays High frequency sound Radio waves Impedance
Types of radiation Ionizing radiation Non-Ionizing radiation Non-Ionizing radiation Non-Ionizing radiation
Contrast High Low High Low
Spatial Resolution 50-200 μm 50-500 μm 25-100 μm Low
Scanning time <20 min < 30 min <40 min <10 min
Cost Moderate Low Very High Low
Size Non portable Portable Non portable Portable
Advantages Bone and tumor imaging, anatomic imaging Visualize muscles, tendon and internal organs Morphological and functional imaging Rapid tomographic imaging, low cost, noninvasive
Disadvantages High cost Ionizing radiation Operator dependency Noisy, cost, low sensitivity Not mature yet

Image reconstruction algorithms_

Reconstruction algorithm Description
Linear approach - Solves the forward problem- Iterations are limited
Simple stage reconstruction - Solves the forward problem- Iterative processm- Very much used
Sheffield Back-projection - Solves the forward problem- The best known for EITm- Not flexible
Newton-Raphson - Solves the forward problem based on EMFm- Inverse problem thanks to matrix sensitivity
Optimization by particle swarms - Solves the inverse problem

Electrical conductivity for Human tissues_

Tissue Conductivity (mS/m)
Cerebrospinal fluid 1450 - 1800
Blood 500 - 650
Scalp 300 - 400
Brain 300 - 420
Muscle 200 - 400
Fat 50
Bone 6
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