Two new neural network approaches to two-dimensional CT image reconstruction
Recently, we developed two neural network techniques for reconstructing two-dimensional CT images from a small number of projection data. They are simulated annealing and back propagation reconstruction techniques. The two techniques have been developed independent of each other. In this paper we present a comparative evaluation study on the two techniques. We start with introducing the two new approaches one by one, and then present simulation results. Reconstruction results by a well known conventional method - Algebraic Reconstruction Technique (ART) - is also presented for the sake of comparison. A quantitative evaluation among the three reconstruction methods is presented. A pixel-wise error estimator is used to calculate the overall error in the reconstructed images. The estimator reveals the effectiveness of the new neural network techniques compared to the conventional technique ART. (C) 1999 Elsevier Science B.V. All rights reserved.
ct image reconstruction simulated annealing art back-propagation energy gap