Reconstruction of Electrical Impedance Tomography Using Fish School Search, Non-Blind Search, and Genetic Algorithm
This is an incremental improvement for medical or environmental imaging, offering faster convergence in EIT reconstruction.
The authors tackled the ill-posed inverse problem of Electrical Impedance Tomography (EIT) image reconstruction by proposing a method based on Fish School Search (FSS) and Non-Blind Search (NBS), which converged faster than genetic algorithms in simulations with numerical phantoms using 415 finite element meshes and 20 runs per configuration.
Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that does not use ionizing radiation, with application both in environmental sciences and in health. Image reconstruction is performed by solving an inverse problem and ill-posed. Evolutionary Computation and Swarm Intelligence have become a source of methods for solving inverse problems. Fish School Search (FSS) is a promising search and optimization method, based on the dynamics of schools of fish. In this article the authors present a method for reconstruction of EIT images based on FSS and Non-Blind Search (NBS). The method was evaluated using numerical phantoms consisting of electrical conductivity images with subjects in the center, between the center and the edge and on the edge of a circular section, with meshes of 415 finite elements. The authors performed 20 simulations for each configuration. Results showed that both FSS and FSS-NBS were able to converge faster than genetic algorithms.