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X-ray phase contrast imaging (XPCI) provides higher sensitivity to contrast between low absorbing objects that can be invisible to conventional attenuation-based X-ray imaging. XPCI's main application has been so far focused on medical areas at relatively low energies (< 100 keV). The translation to higher energy for industrial applications, where energies above 150 keV are often needed, is hindered by the lack of masks/gratings with sufficiently thick gold septa. Fabricating such structures with apertures of tens of micrometers becomes difficult at depths greater than a few hundreds of micrometers due to aspect ratio dependent effects such as anisotropic etching, and preferential gold (Au) deposition at the top of the apertures. In this work, these difficulties are overcome by Deep Reactive Ion Etching optimized by a stepped parameters approach and bismuth-mediated superconformal filling of Au, ultimately resulting in 500 micrometers deep silicon masks filled with Au at bulk density. The obtained masks, tested in an Edge Illumination XPCI system with a conventional source and a photon-counting detector, show good agreement with simulations at different energy thresholds. They also demonstrate a higher phase sensitivity for highly absorbing objects when compared to lower aspect ratio masks, proving their potential for industrial non-destructive testing., Comment: 6 figure plus table of content at the end

Recent innovations in x-ray technology (namely phase-based and energy-resolved imaging) offer unprecedented opportunities for material discrimination, however they are often used in isolation or in limited combinations. Here we show that the optimized combination of contrast channels (attenuation at three x-ray energies, ultra-small angle scattering at two, standard deviation of refraction) significantly enhances material identification abilities compared to dual-energy x-ray imaging alone, and that a combination of off-the-shelf machine learning approaches can effectively discriminate e.g., threat materials in complex datasets. The methodology is validated on a range of materials and image dataset that are both an order of magnitude larger than those used in previous studies. Our results can provide an effective methodology to discriminate, and in some cases identify, different materials in complex imaging scenarios, with prospective applications across the life and physical sciences. While the detection of threat materials is used as a demonstrator here, the methodology could be equally applied to e.g., the distinction between diseased and healthy tissues or degraded vs. pristine materials., Comment: 23 pages - 13 main text, 10 supplementary 11 figures - 5 main text, 6 supplementary

X-ray dark-field imaging creates a representation of the sample where contrast is generated by subresolution features within the volume under inspection. These are detected by a local measurement of the radiation field's angular distribution, and how it is affected by the interaction with matter. X-ray dark-field imaging typically requires taking multiple exposures for separating the contributions to the detected X-ray intensity arising from scattering, refraction and attenuation; a procedure often called phase retrieval. We propose an approach to retrieve an X-ray dark-field image from a single X-ray shot. We demonstrate the method using a laboratory-based, rotating anode X-ray tube system without the need for coherent radiation or a high-resolution detector. This reduces the complexity of data acquisition, enabling faster scanning and increasing dose efficiency. Moreover, our approach reduces the problem dimensionality by one, with substantial implications for data-intensive applications like tomography. The model assumes a homogeneous material, and we show this is a valid hypothesis for soft biological tissues by reconstructing dark-field tomography images from data sets containing a single shot per view. We believe our method to be broadly applicable and relevant for many X-ray dark-field imaging implementations, including fast radiography, directional dark-field and for use with pulsed X-ray sources., Comment: 7 pages, 3 figures with additional supplemental material of 3 pages, 3 figures

X-ray dark-field or ultra-small angle scatter imaging has become increasingly important since the introduction of phase-based x-ray imaging and is having transformative impact in fields such as in vivo lung imaging and explosives detection. Here we show that dark-field images acquired with the edge-illumination method (either in its traditional double mask or simplified single mask implementation) provide a direct measurement of the scattering function, which is unaffected by system-specific parameters such as the autocorrelation length. We show that this is a consequence both of the specific measurement setup and of the mathematical approach followed to retrieve the dark-field images. We show agreement with theoretical models for datasets acquired both with synchrotron and laboratory x-ray sources. We also introduce a new contrast mechanism, the variance of refraction, which is extracted from the same dataset and provides a direct link with the size of the scattering centres. We show that this can also be described by the same theoretical models. We study the behaviour of both signals vs. key parameters such as x-ray energy and scatterer radius. We find this allows quantitative, direct, multi-scale scattering measurements during imaging, with implications in all fields where dark-field imaging is used., Comment: 18 pages, 5 main figures, 6 extended data figures