A reconstruction-free projection selection procedure for binary tomography using convolutional neural networks

Abstract

In discrete tomography sometimes it is necessary to reduce the number of projections used for reconstructing the image. Earlier, it was shown that the choice of projection angles can significantly influence the quality of the reconstructions. In this study, we apply convolutional neural networks to select projections in order to reconstruct the original images from their sinograms with the smallest possible error. The training of neural networks is generally a time-consuming process, but after the network has been trained, the prediction for a previously unseen input is fast. We trained convolutional neural networks using sinograms as input and the desired, algorithmically determined k-best projections as labels in a supervised setting. We achieved a significantly faster projection selection and only a slight increase in the Relative Mean Error (RME).