Explosives detection improved by new X-ray technique

Some explosives can be difficult to spot using conventional X-ray alone, and the new method could revolutionise how illicit materials such as narcotics, illegal wildlife and explosives are detected. The findings have significant implications for the security sector, with potential to support healthcare and industry.

For the paper, published in Nature Communications, the engineers combined a new X-ray measuring technique with AI machine learning, and tested it in a custom-developed X-ray security scanner, using objects containing hidden explosive material as well as safe objects.

Study lead Professor Olivo had discovered through his earlier research that microscopic changes or irregularities in objects cause X-ray beams to bend as they pass through them. His current work explores the potential applications of this phenomenon through his Chair in Emerging Technologies, funded by the Royal Academy of Engineering. The new method relies on measuring these tiny bends as the beam moves through objects of different textures.

Senior author Professor Sandro Olivo (UCL Medical Physics & Biomedical Engineering), explained: “This is a radically different way of inspecting materials and objects by analysing textures, and allows us a new way of detecting illicit materials. The tiny bends in X-rays have always been there, but they are invisible to conventional X-ray systems, so this allows us to access a huge amount of previously untapped information.

“So far, we have shown it works extremely well for detecting explosives, but it could be used in any application that relies on X-rays, such as medical imaging or detecting weaknesses in industrial components.”

The small deviation in an X-ray beam occurs at angles as small as a microradian, which is about 20,000 times smaller than a degree. The team combined the measurement of these angles, known as microradian scatter, with AI to accurately identify objects and materials through their texture. When tested on explosives, the detection rate was 100%.

The team tested the technique by introducing X-ray masks in scanners to detect scattering. This produced highly-detailed images where every pixel showed the degree of microscopic irregularity in the object. Images differed according to their microscopic structures, which wouldn’t be visible on conventional X-ray images. The team were able to distinguish between dangerous and benign material by analysing the microscopic irregularities.

Co-author Tristram Riley-Smith (Founder, XPCI Technology and UKRI Champion for Conflict, Crime & Security) said: “This research has demonstrated the potential to transform the detection of covert threats around the world, as well as such varied contraband as narcotics and illicit wildlife commodities.”

Co-author David Bate (Nikon and UCL Medical Physics & Biomedical Engineering) said: “Through our EPSRC-funded Prosperity Partnership, we are working with Professor Olivo’s team to bring this transformative technology into the industrial field to improve quality and safety. By training the AI on ‘perfect’ components, we predict that the technique can be used to identify defects in industrial components such as cracks, rust or gaps before they are visible to the naked eye.”

Trevor Francis, Chairman, Innovative Research Call (IRC) for Explosives and Weapons Detection, who supported the work, said: “What Professor Olivo and the team have achieved with their innovative approach not only has the potential to enhance security applications for detecting explosives and weapons, but with their technique applied to other materials, such as illicit drugs, could also positively impact additional end-user communities, such as customs.”

The project was supported by the UK Government and the US Department of Homeland Security, Science and Technology Directorate through the IRC funding scheme, the Royal Academy of Engineering through Professor Olivo’s RAEng Chair in Emerging Technologies and the Engineering and Physical Sciences Research Council (EPSRC).

Links

• Research paper Enhanced detection of threat materials by dark-field X-ray imaging combined with deep neural networks in Nature Communications
• Professor Sandro Olivo’s academic profile
• UCL Medical Physics & Biomedical Engineering
• UCL Engineering

Image

• iStock. Credit: EvgeniyShkolenko

Media contact

Kate Corry

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Email: k.corry@ucl.ac.uk