Neural networks for computed tomography

Proposes a novel application of neural networks to cognitive tasks of computed tomography (CT). The principle of Hopfield type neural networks to reconstruct the image from the projected densities is described. The technique of reconstruction is based on the algebraic reconstruction technique (ART). Simulation results for a model of an image, where the reconstruction space was divided into 32*32 elements with 9 color degrees, show the satisfactory performance in terms of accuracy and computation time by application of the neural networks.<
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