Image reconstruction of computer tomography from a few views based on a Gaussian machine

A new reconstruction algorithm of computer tomography (CT) from a few views based on a neural network of Gaussian Machine (GM) is presented. The problem of image reconstruction is formulated as optimization under the criterion of maximum entropy, and a GM is then constructed to solve the optimization problem using simulated annealing technique with hyperbolic temperature adjustment. We demonstrate both the Simultaneous Algebraic Reconstruction Technique (SART) reconstruction of this image and the GM reconstruction using the same measured input data. The effect of noise in the projection data, projection angles and sample intervals are addressed. The results of numerical simulation show that this technique using the projection data obtained from four views with the projection angles 45°apart has fairly high accuracy (the average relative error is 0.03%) and good stability against noise.