It is a well-known fact that exposure of living tissues to ionizing radiation can result on several health problems, where cancer is probably the most complicated. This issue has been strengthening the efforts of both academy and industry to develop and improve non-invasive methods in the last decades. Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that offers a vast field of possibilities due to its low cost, portability, and safety of handling. However, EIT image reconstruction is an ill-posed problem governed by Poisson's Equation:there are no unique mathematical solution to solve this equation. Herein this work we present an EIT reconstruction method based on the optimization of the relative error of reconstruction using a modified differential evolution algorithm. Our approach was compared with genetic algorithms, classical differential evolution, and other modified differential evolution strategies. Results were quantitatively evaluated with ground-truth images using the relative mean squared error, showing that our results reached considerably low error magnitudes. Qualitative evaluation also indicated that our results were anatomically consistent.

The development and improvement of non-invasive imaging techniques have been increasing in the last decades, due to interests from both academy and industry. Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that offers a vast field of possibilities due to its low cost, portability, and safety of handling. However, EIT image reconstruction is an ill-posed problem. Herein this work we present an EIT reconstruction method based on the optimization of the relative error of reconstruction using genetic algorithms employing elitist strategies. The initial set of solutions used by the elitist genetic algorithm includes a noisy version of the solution obtained from the backprojection algorithm, according to Saha and Bandyopadhyay's criterion for non-blind initial search in optimization algorithms, in order to accelerate convergence and improve performance.

The fields of non-invasive imaging and e-health have been increasing in the last decades, due to the need of avoiding to exposure living tissues to ionizing radiation, increasing monitoring levels of critical patients, and promoting the increasing of quality life. Furthermore, the use of image-reconstruction devices based on ionizing radiation can result on several health problems for patients in case non-calibrated apparatus is employed. These needs have been strengthening the efforts to improve non-invasive methods like Electrical Impedance Tomography (EIT), a low-cost, non-invasive, portable, and safe of handling imaging technique. However, EIT image reconstruction is still an open problem, due to its nature as an ill-posed problem governed by the Equation of Poison. Several numerical methods are used in order to solve this equation without generating anatomically inconsistent results. Evolutionary methods can be used as alternatives to Gauss-Newton and Backprojection well-known approaches, which frequently generate low-resolution blurred images. Herein this work we present an EIT reconstruction method based on the optimization of the relative error of reconstruction using particle swarm optimization with non-blind search. We studied two forms of initialization: totally random and including an imperfect but anatomically consistent solution based on Gauss-Newton reconstruction method, according to Saha and Bandyopadhyay's criterion for non-blind initial search in optimization algorithms, in order to guide the iterative process to avoid anatomically inconsistent solutions. Our approach was compared with genetic algorithms. Results were quantitatively evaluated with groundtruth images using the relative mean squared error, showing that our results reached low error magnitudes. Qualitative evaluation also indicated that our results were morphologically consistent.

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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 we 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. We performed 20 simulations for each configuration. Results showed that both FSS and FSS-NBS were able to converge faster than genetic algorithms.

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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 and bioinspired computation have become a source of methods for solving inverse problems. In this chapter, the authors investigate the performance of fish school search (FSS) and differential evolution (DE) using non-blind search (NBS) considering meshes of 415, 3190, and 9990 finite elements. The methods were evaluated using numerical phantoms consisting of electrical conductivity images with objects in the center, between the center and the edge, and on the edge of a circular section. Twenty simulations were performed for each configuration. Results showed that both FSS and DE are able to perform EIT image reconstruction with large meshes and converge faster by using non-blind search.