Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks

Dark-field X-ray imaging is sensitive to the microstructure of a material. Here, the authors combine this with a neural network algorithm to provide efficient material discrimination, e.g., of explosives vs non-threat materials. 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.

Automated summary

Dark-field X-ray imaging combined with a neural network algorithm enables efficient material discrimination, which has significant applications in security inspections and other fields.