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Electrical Impedance Tomography (EIT) is a noninvasive imaging technique thatdoes not use ionizing radiation, with application both in environmentalsciences and in health. Image reconstruction is performed by solving an inverseproblem and ill-posed. Evolutionary Computation and Swarm Intelligence havebecome a source of methods for solving inverse problems. Fish School Search(FSS) is a promising search and optimization method, based on the dynamics ofschools of fish. In this article the authors present a method forreconstruction of EIT images based on FSS and Non-Blind Search (NBS). Themethod was evaluated using numerical phantoms consisting of electricalconductivity images with subjects in the center, between the center and theedge and on the edge of a circular section, with meshes of 415 finite elements.The authors performed 20 simulations for each configuration. Results showedthat both FSS and FSS-NBS were able to converge faster than genetic algorithms.

Reconstruction of Electrical Impedance Tomography Using Fish School Search, Non-Blind Search, and Genetic Algorithm