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EVALUATION OF ITERATIVE ALGORITHMS FOR TOMOGRAPHY IMAGE RECONSTRUCTION

The greatest impact of the tomography technology currently occurs in medicine. The success is due to the fact that human body presents standardized dimensions with well-established composition. These conditions are not found in industrial objects. In industry, there is a great deal of interest in using the tomography in order to know the inner part of (i) manufactured industrial objects or (ii) the machines and their means of production. In these cases, the purpose of the tomography is:  (a) to control the quality of the final product and (b) to optimize the production, contributing to the pilot phase of the projects and analyzing the quality of the means of production. This scan system is a non-destructive, efficient and fast method for providing sectional images of industrial objects and it is able to show the dynamic processes and the dispersion of the materials structures within these objects. In this context, it is important that the reconstructed image may present a great spatial resolution with a satisfactory temporal resolution. Thus, the algorithm to reconstruct the images has to meet these requirements. This work consists in the analysis of three different iterative algorithm methods, namely the Maximum Likelihood Estimation Method (MLEM), the Maximum Likelihood Transmitted Method (MLTR) and the Simultaneous Iterative Reconstruction Method (SIRT. The analyses involved the measurement of the contrast to noise ratio (CNR), the root mean square error (RMSE) and the Modulation Transfer Function (MTF),in order to know which algorithm fits the conditions to optimize the system better.   The algorithms and the image quality analyses were performed by Matlab® 2013b.