Department of Medical Physics and Biomedical Engineering, UCL, London, WC1E 6BT, UK.
Nikon X-Tek Systems Ltd, Tring, Herts, HP23 4JX, UK.
Nat Commun. 2022 Sep 9;13(1):4651. doi: 10.1038/s41467-022-32402-0.
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.
X 射线成像是通过引入基于相位的方法得到增强的。在相位对比图像中，细节可见度得到提高，而暗场图像对低于系统空间分辨率的长度尺度上的非均匀性敏感。在这里，我们表明暗场会产生一种与被成像材料特征相关的纹理，并且将其与传统的衰减相结合，可以提高对威胁材料的区分能力。我们表明，通过利用暗场和衰减信号的不同能量依赖性，可以解决剩余的歧义。此外，我们通过两项概念验证研究证明，暗场纹理非常适合通过机器学习方法进行识别。在这两种情况下，将相同的方法应用于从其中去除暗场图像的数据集，都会导致性能明显下降。虽然这些研究的规模较小，需要进一步研究，但结果表明，在安全应用和其他领域中，暗场和深度神经网络的联合使用具有潜力。
