Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks

Abstract

X-ray Computed Laminography (CL) is a well-known computed tomography technique to image the internal structure of flat objects. High-quality CL imaging requires, however, a large number of X-ray projections, resulting in long acquisition times. Reducing the number of acquired projections allows to speed up the acquisition process but decreases the quality of the reconstructed images. In this work, we investigate the use of Convolutional Neural Networks for processing volumes reconstructed from only four X-ray projections acquired at an inline CL scanning setup.