@inproceedings{2e51854f826a4db69da0af68eaed9e7e,
title = "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.",
keywords = "3D imaging, Computed Laminography, Convolutional Neural Networks, Deep Learning",
author = "Pereira, {Luis F.Alves} and {De Beenhouwer}, Jan and Johann Kastner and Jan Sijbers",
note = "Funding Information: This work was supported by the Comet-Project (grant ID: 871974) ”Photonic Sensing for Smarter Processes” financed by FFG and the federal government of Upper Austria and Styria, as well as by the FWO-SBO project MetroFlex (S004217N). Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020 ; Conference date: 09-11-2020 Through 11-11-2020",
year = "2020",
month = nov,
doi = "10.1109/ICTAI50040.2020.00099",
language = "English",
series = "Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI",
publisher = "IEEE Computer Society",
pages = "612--616",
editor = "Miltos Alamaniotis and Shimei Pan",
booktitle = "Proceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020",
address = "United States",
}