Researchers detect hidden threats with advanced x-ray imaging

 Researchers have combined various x-ray imaging technologies to create multi-contrast images that can be used to detect threatening materials such as explosives in thousands of complicated scenarios. The new approach, which also leverages readily available machine learning procedures for materials classification, could be useful for security screening as well as applications in the life and physical sciences.

"This method is particularly well suited to discriminating objects with very similar elemental composition," said research team leader Thomas Partridge from University College London in the UK. "It could be used in airport security or any inline scanning operation to examine materials flagged as suspicious by an initial fast scan such as a traditional x-ray system."

In Optica, Optica Publishing Group's journal for high-impact research, the researchers show that the new approach was highly effective in accurately detecting and identifying explosives in almost 4,000 scans of threatening and non-threatening materials hidden inside bags or obscured by various types of objects. They achieved a near-perfect recall rate of 99.68%, with just one false negative, from the threat-bearing cases.

"Although more work is needed, this approach could also prove useful for medical imaging," said Partridge. "While traditional x-ray imaging struggles to separate healthy from diseased tissue, other studies have suggested that phase contrast imaging might be able to capture textures that could be used to distinguish healthy and benign tissues."

Unlocking material secrets

The x-ray machines found in airports or medical facilities are based on x-ray attenuation, which images the reduction in intensity in x-rays after they pass through a material. The new technique creates multi-contrast images by combining conventional x-ray attenuation data at various x-ray energies with x-ray phase information, which consists of refraction and dark-field channels.

"Many explosives and common everyday items are composed of primarily carbon, hydrogen, nitrogen and oxygen, a similarity that makes them difficult to separate with x-ray attenuation alone," said Partridge. "The additional channels offer significantly better enhancement of edges as well as textures and grains of materials, allowing the discrimination of objects with very similar elemental compositions."

Source: ScienceDaily