Partridge, T;
Astolfo, A;
Shankar, SS;
Vittoria, FA;
Endrizzi, M;
Arridge, S;
Riley-Smith, T;
... Olivo, A; + view all
(2022)
Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks.
Nature Communications
, 13
(1)
, Article 4651. 10.1038/s41467-022-32402-0.
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Abstract
X-ray imaging has been boosted by the introduction of phase-based methods. Detail visibility is enhanced in phase contrast images, and dark-field images are sensitive to inhomogeneities on a length scale below the system’s spatial resolution. Here we show that dark-field creates a texture which is characteristic of the imaged material, and that its combination with conventional attenuation leads to an improved discrimination of threat materials. We show that remaining ambiguities can be resolved by exploiting the different energy dependence of the dark-field and attenuation signals. Furthermore, we demonstrate that the dark-field texture is well-suited for identification through machine learning approaches through two proof-of-concept studies. In both cases, application of the same approaches to datasets from which the dark-field images were removed led to a clear degradation in performance. While the small scale of these studies means further research is required, results indicate potential for a combined use of dark-field and deep neural networks in security applications and beyond.
| Type: | Article |
|---|---|
| Title: | Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41467-022-32402-0 |
| Publisher version: | https://doi.org/10.1038/s41467-022-32402-0 |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Characterization and analytical techniques, Imaging techniques, Machine learning, X-rays |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10155649 |
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