ArticleNature communications2022
Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Microfluidic Gratings for X-Ray Phase Contrast Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- On the optimization of the geometric pattern for structured illumination based X-ray phase contrast and dark-field imaging: a simulation study and its experimental validation.Journal of synchrotron radiation · 2026Article
- A Benchmark Dataset for Concealed Improvised Explosive Device Detection in X-ray Security Imaging.Scientific data · 2026Article
- Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning.Light, science & applications · 2026Article
- Synchrotron radiation based analyzer-free dual-phase dark-field lung imaging approach for radiography and CT.Scientific reports · 2025Article
- Multi-contrast machine learning improves schistosomiasis diagnostic performance.PLoS neglected tropical diseases · 2025Article
- Hybrid dark-field and attenuation contrast retrieval for laboratory-based X-ray tomography.Optica · 2024Article
- Edge-illumination spectral phase-contrast tomography.Physics in medicine and biology · 2024Article
- Laboratory-based x-ray dark-field microscopy.Physical review applied · 2023Article
- On the equivalence of the X-ray scattering retrieval with beam tracking and analyser-based imaging using a synchrotron source.Journal of physics D: Applied physics · 2023Article
- Enhanced detonators detection in X-ray baggage inspection by image manipulation and deep convolutional neural networks.Scientific reports · 2023Article
- Femtosecond multimodal imaging with a laser-driven X-ray source.Communications physics · 2023Article
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10 authors.
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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.
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