Fig.1
Fig. 2
Fig. 3
![Chest image reconstruction by EIT [21].](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/64721e8e215d2f6c89dbc94e/j_joeb-2021-0007_fig_003.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA6AP2G7AKOUXAVR44%2F20251208%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20251208T104838Z&X-Amz-Expires=3600&X-Amz-Signature=697a775d3c52a4aaa4d7fb57952d490d8e941c2a98e383fd6559a6f9d6f69f99&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
Fig. 4
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].](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/64721e8e215d2f6c89dbc94e/j_joeb-2021-0007_fig_005.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA6AP2G7AKOUXAVR44%2F20251208%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20251208T104838Z&X-Amz-Expires=3600&X-Amz-Signature=26da15b68c910128a446372d3afe8f6fa88df4b824bddf025dc0f06f7a7fff73&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
Fig. 6
![Series of dynamic images showing air filling during inspiration by the PulmoVista500 system [81].](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/64721e8e215d2f6c89dbc94e/j_joeb-2021-0007_fig_006.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA6AP2G7AKOUXAVR44%2F20251208%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20251208T104838Z&X-Amz-Expires=3600&X-Amz-Signature=d74850a2ff40f84dfad3ba6f923837f992bd9031feada36ad08257609522203c&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
Fig. 7
![Cardiac monitoring by CardioInspect system [88].](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/64721e8e215d2f6c89dbc94e/j_joeb-2021-0007_fig_007.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA6AP2G7AKOUXAVR44%2F20251208%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20251208T104838Z&X-Amz-Expires=3600&X-Amz-Signature=890eb66d3bb73e92f629345ebea205d5cda989ae51a8e4ee5fd454c654b638ff&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
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 |
| Simple stage reconstruction | - Solves the forward problem |
| Sheffield Back-projection | - Solves the forward problem |
| Newton-Raphson | - Solves the forward problem based on EMFm |
| 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 |