A Review of Electrical Impedance Tomography in Lung Applications: Theory and Algorithms for Absolute Images.

Electrical Impedance Tomography (EIT) is under fast development, the present paper is a review of some procedures that are contributing to improve spatial resolution and material properties accuracy, admitivitty or impeditivity accuracy. A review of EIT medical applications is presented and they were classified into three broad categories: ARDS patients, obstructive lung diseases and perioperative patients. The use of absolute EIT image may enable the assessment of absolute lung volume, which may significantly improve the clinical acceptance of EIT.

Electrical impedance tomography reconstruction through simulated annealing with multi-stage partially evaluated objective functions.

The EIT reconstruction problem can be solved as an optimization problem using Simulated Annealing. Different objective functions have already been used: Euclidian distance between the simulated and observed potentials; total least squares error minimization. The objective function was partially evaluated in both methods.

Electrical impedance tomography reconstruction through simulated annealing with total least square error as objective function.

The EIT reconstruction problem can be solved as an optimization problem where the divergence between a simulated impedance domain and the observed one is minimized. This optimization problem can be solved by a combination of Simulated Annealing (SA) for optimization and Finite Element Method (FEM) for simulation of the impedance domain. This combination has usually a very high computational cost, since SA requires an elevated number of objective function evaluations and those, obtained through FEM, are often expansive enough to make the whole process inviable.

Image reconstruction using interval simulated annealing in electrical impedance tomography.

Electrical impedance tomography (EIT) is an imaging technique that attempts to reconstruct the impedance distribution inside an object from the impedance between electrodes placed on the object surface. The EIT reconstruction problem can be approached as a nonlinear nonconvex optimization problem in which one tries to maximize the matching between a simulated impedance problem and the observed data. This nonlinear optimization problem is often ill-posed, and not very suited to methods that evaluate derivatives of the objective function.

Electrical impedance tomography reconstruction through Simulated Annealing with incomplete evaluation of the objective function.

The EIT reconstruction problem is approached as an optimization problem where the difference between a simulated impedance domain and the observed one is minimized. This optimization problem is often solved by Simulated Annealing (SA), but at a large computational cost due to the expensive evaluation process of the objective function. We propose here, a variation of SA applied to EIT where the objective function is evaluated only partially, while ensuring upper boundaries on the deviation on the behavior of the modified SA.