Image reconstruction using simulated annealing in electrical impedance tomography: a new approach

Figures & data

Figure 1. Numerical phantom used to simulate the data acquired by an EIT system: (a) Electrodes distribution and (b) domain discretization.

Figure 2. Phantom conductivity distributions. The areas in red have conductivity equal to 5 conductivity measurement units (cmu) and the blue colour region has conductivity equal to 2 cmu.

Figure 3. Mesh used to solve EIT inverse problem.

Figure 4. Reconstructions of domains presented on Figure 2 using the proposed SA approach. (a) Reconstruction for Figure 2(a); (b) reconstruction for Figure 2(b) and (c) reconstruction for Figure 2(c).

Figure 5. Reconstructions of domains presented on Figure 2 using Herrera’s approach. (a) Reconstruction for Figure 2(a); (b) reconstruction for Figure 2(b) and (c) reconstruction for Figure 2(c).

Figure 6. Acceptance ratio as a function of the number of iterations for the reconstructions of Figure 4. In red, result for the reconstruction of Figure 4(a). In blue, result for the reconstruction of Figure 4(b). In green, result for the reconstruction of Figure 4(c).

Figure 7. Objective function behaviour for reconstruction showed in Figure 3(a).

Figure 8. Data acquisition system and tank used for the tests.

Figure 9. (a) Mesh used in the image reconstruction from experimental data. (b) Schematic representation of the electrodes positions in the tank.

Figure 10. (a) First experimental arrangement. (b) Reconstructed image of experimental data.

Figure 11. (a) Second experimental arrangement. (b) Reconstructed image of experimental data.

Figure 12. (a) Third experimental arrangement. (b) Reconstructed image of experimental data.