Reconstructing images in electrical impedance tomography using hybrid genetic algorithms - Mendoza, Renier G. (Science Diliman, 2012)
In electrical impedance tomography (EIT), current patterns Il (l = 1,2, ..., L) are injected to a body through L electrodes and the corresponding voltages Vl (l = 1,2, ..., L) are then measured. Based on these measurements, image reconstruction is done by solving an inverse problem involving the generalized Laplace equation -▽· (σ-▽ɸ) = 0 on Ώ, where (σ is the conductivity distribution and ɸ is the electric potential over .Ώ . Using the known quantities I1 and V1, we wish to recover the geometry and conductivity σ within the body. In this work, we considered the case where the body . Ώ is a unit square and has two square-shaped cavities with constant conductivities. The forward problem was solved using the finite element method while the inverse problem was posed as a minimization problem. We then explored the use of the genetic algorithm (GA) hybridized with another heuristic algorithm (simulated annealing) and two deterministic algorithms (Neider-Mead simplex method and Quasi-Newton method) in solving the optimization problem. Our numerical simulations showed that the proposed hybrids of the GA could best recover the conductivity distribution if the geometry inside the body was known. [ABSTRACT FROM AUTHOR]