X-ray Transmission Imaging Research Articles

Transformer-based dual-view X-ray security inspection image analysis

Artificial intelligence technology is rapidly advancing and has been widely applied in the field of intelligent security inspection. Utilizing computer vision technology to detect prohibited items in X-ray images has drawn much attention. Due to the transmission effect of X-rays, single-view security inspection images are prone to object occlusion and poor imaging angles, which seriously affects the performance of object detection models. Dual-view security inspection equipment can simultaneously capture X-ray transmission images of the item under inspection from both horizontal and vertical angles, which can effectively address issues of poor imaging angles and object occlusions that single-view imaging cannot resolve. In this paper, we introduced the artificial intelligence technology in dual-view security inspection image analysis, and proposed the dual-view feature fusion and prohibited item detection model in X-ray security inspection images based on the Vision Transformer framework. The detection model contains two input channels: the main and secondary channel. The main function of the main channel is to detect prohibited items in security inspection images, while the secondary channel is dedicated to providing effective feature information of prohibited items for the main channel. Two feature interaction modules are applied in the proposed model to realize dual channel information exchange and supplement from local and global perspectives respectively. Simulation results based on the public Dualray dataset have demonstrated the state-of-the-art performance of the proposed dual-view X-ray image detection model. Code is available at https://github.com/zhg-SZPT/Trans2Ray.

Spatiotemporal dynamics of fast electron heating in solid-density matter via XFEL

High-intensity, short-pulse lasers are crucial for generating energetic electrons that produce high-energy-density (HED) states in matter, offering potential applications in igniting dense fusion fuels for fast ignition laser fusion. High-density targets heated by these electrons exhibit spatially non-uniform and highly transient conditions, which have been challenging to characterize due to limitations in diagnostics that provide simultaneous high spatial and temporal resolution. Here, we employ an X-ray Free Electron Laser (XFEL) to achieve spatiotemporally resolved measurements at sub-micron and femtosecond scales on a solid-density copper foil heated by laser-driven fast electrons. Our X-ray transmission imaging reveals the formation of a solid-density hot plasma localized to the laser spot size, surrounded by Fermi degenerate, warm dense matter within a picosecond, and the energy relaxation occurring within the hot plasma over tens of picoseconds. These results validate 2D particle-in-cell simulations incorporating atomic processes and provide insights into the energy transfer mechanisms beyond current simulation capabilities. This work significantly advances our understanding of rapid fast electron heating and energy relaxation in solid-density matter, serving as a key stepping stone towards efficient high-density plasma heating and furthering the fields of HED science and inertial fusion energy research using intense, short-pulse lasers.

3D X-ray computed tomography in the analyses of intraocular lenses explanted because of postoperative opacification.

To use X-ray computed tomography (CT) -which describes the acquisition and reconstruction of 2-dimensional X-ray transmission images to create a 3D representation of a specimen -in the analyses of intraocular lenses (IOLs) explanted because of optical opacification occurring postoperatively. John A. Moran Eye Center, and Utah Nanofab, University of Utah, Salt Lake City, Utah. Laboratory study. A hydrophilic acrylic and a silicone lens (the latter from an eye with asteroid hyalosis) explanted because of postoperative calcification, as well as a poly(methyl methacrylate) (PMMA) lens explanted because of snowflake degeneration underwent analysis under gross and light microscopy. Then, they were attached to an appropriate support and scanned under a Zeiss Xradia Versa X-ray microscope. After data acquisition, data segmentation was performed with a commercially available program to separate image data into components. Morphology, size/volume, and specific location of calcified deposits on the surface or within the substance of explanted IOLs could be demonstrated by X-ray CT within the entire volume of each lens with high contrast and resolution. The PMMA lens showed multiple spaces/fissures in relation to Nd:YAG pitting of the optic, and what appeared to be sheets of delaminated PMMA material at different levels within the optic substance. The key benefit of X-ray CT is that it can be performed without physically sectioning the specimen. This preliminary study demonstrates that this technology can be potentially useful in the imaging and analyses of explanted, opacified lenses.

X-ray transmission imaging of waste printed circuit boards for value estimation in recycling using machine learning.

The growing amount of electronic waste is a global challenge: on one hand, it poses a threat to the environment as it may contain toxic or hazardous substances, on the other hand it is a valuable 'urban mine' containing metals like gold and copper. Thus, recycling of electronic waste is not only a measure to reduce environmental pollution but also economically reasonable as prices for raw materials are rising. Within electronic waste, printed circuit boards (PCBs) occupy a prominent position, as they contain most of the valuable material. One important step in the overall recycling process is the evaluation and the value estimation for further treatment of the waste PCBs (WPCBs). In this article, we introduce a method for value estimation of entire WPCBs based on component detection. The value of the WPCB is then predicted by the value of the detected components. This approach allows a flexible application to different situations. In the first step, we created a dataset and labelled the components of 104 WPCBs using different component classes. The component detection is performed on dual energy X-ray images by the deep neural object detection network 'YOLO v5'. The dataset is split into a training, validation and test subset and standard performance measures as precision, recall and F1-score of the component detection are evaluated. Representative samples from all component classes were selected and analysed for the valuable materials to provide the ground truth of the value estimation in the subsequent step.

Imaging simulation for integrated handheld X-ray transmission and backscatter imaging system with a disk scanner

X-ray imaging technology can be divided into transmission and backscatter according to the signal receiving method, both are widely used security technology. In scenarios where it is not easy to utilize large equipment, there is a need for integrated handheld X-ray transmission and backscatter imaging equipment. Existing handheld integrated imaging devices often only add transmission signal receivers to handheld backscatter devices, and their transmission imaging capabilities are insufficient. In order to explore whether integrated handheld X-ray imaging equipment can be further optimized, a disk scanner for handheld X-ray integrated imaging system was designed and imaging simulation was performed. To improve the inadequate transmission capability of existing handheld X-ray machines, the transmission performance is strengthened by setting up transmission windows, which is capable of performing both flying line transmission and flying spot backscattering, overcoming the disadvantage of the existing equipment that can only use flying line scanning or flying spot scanning only. A dynamic simulation imaging method is developed to study the feasibility of the system, which includes the ray source simulation, the scanning process simulation, and the imaging simulation. The disk scanner with windows designed for the handheld X-ray transmission and backscattering integrated imaging system greatly enhances the transmission capability by sacrificing the backscattering capability through the addition of a window structure, and has a core advantage in the penetration resolution test. Its performance is evaluated by simulating the final imaging through the simulation of backscatter spatial resolution test, backscatter line resolution test, transmission resolution test and transmission penetration resolution test items.

Research on the Contrast Enhancement Algorithm for X-ray Images of BiFeO3 Material Experiment

High-Temperature Materials Science Experiment Cabinet on the Chinese Space Station is mainly used to carry out experimental research related to high-temperature materials science in microgravity. It is equipped with an X-ray transmission imaging module, which is applied to realize transmission imaging of material samples under microgravity. However, the X-ray light source is far away from the experimental samples, and the images obtained by the module are blurred, so it is impossible to accurately observe the morphological changes during the melting and solidification processes of high-temperature materials. To address this issue, this paper proposed a contrast enhancement algorithm specifically designed for X-ray images obtained during the experiments of high-temperature materials. The algorithm is based on gradient three-interval equalization, and it is combined with a Gaussian function to expand the gradient histogram. Meanwhile, the local gray level information within each gradient interval is corrected by designing an improved adaptive contrast enhancement algorithm. By comparing with Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms, EnlightenGAN, and Wavelet algorithms, the Contrast Enhancement based contrast-changed Image Quality measure (CEIQ) and Measure of Enhancement (EME) are improved by an average of 56.97%, 10.58%, and Measure of Entropy (MOE) are improved by an average of 7.74 times. The experimental results show that the algorithm makes the image details clearer on the basis of image contrast enhancement. The solid-liquid interface in the image can be clearly observed after contrast enhancement. The algorithm provides strong support for the study of interface dynamics during the experiment process of high-temperature materials.

Development of prototype backscatter X-ray security scanner for luggage inspection

In the Republic of Korea, for the purpose of security screening at ports and airports, the present system for inspecting small cargo/luggage relies on transmission X-ray imaging, with which discernment of organic and powder-form materials in images is difficult. Backscatter X-ray imaging techniques, contrastingly, are sensitive to organic materials (i.e., low-Z elements) due to a higher probability of Compton scattering relative to other photon interactions. Such techniques, therefore, have the potential to be used in security screening systems for the detection of organic compounds such as drugs and explosives. In the present study, a prototype backscatter X-ray security scanner for luggage screening was developed, and its performance was evaluated at various tube voltages. It was found that the isolation contrast, which indicates the minimum discernible thickness of a plastic panel when the background material of the image is steel, was approximately 1 mm at the tube voltage of 120 kVp. Moreover, amorphously shaped contraband articles randomly hidden inside the luggage were clearly visible at tube voltages ranging from 80 to 160 kVp. The developed backscatter X-ray security scanner is expected to provide improved detection efficiency for thin objects and/or illicit organic materials compared to a transmission-based X-ray imaging system.

In-line sorting system with battery detection capabilities in e-waste using combination of X-ray transmission scanning and deep learning

In the recycling process of e-waste, fires caused by the accidental crushing of batteries are a serious problem. Currently, the presence of batteries in e-waste is estimated based on the experience of the staff involved, which lacks speed and accuracy. To solve this problem, an in-line sorting system that can detect batteries by using a combination of X-ray scanning and deep learning was developed. The novel and unique feature of this system is its three-stage deep learning processing. First, the type of e-waste item is estimated from X-ray transmission images. Second, the batteries are detected by networks pre-trained specifically for the estimated item types. And third, batteries overlooked in the image process are detected by a follow-up network trained by a variety of situations. Through a validation study, it was confirmed that the program achieved high accuracy (0.967 for trained e-waste categories and 0.770 for untrained), surpassing a comparative program with a single deep learning network (0.902 for trained e-waste categories and 0.716 for untrained).

Quantitative analysis of thin metal powder layers via transmission X-ray imaging and discrete element simulation: Blade-based spreading approaches

Spreading uniform and dense layers is of paramount importance to creating high-quality components using powder bed fusion additive manufacturing (PBF AM). Blade-like tools are often employed for spreading powder metal feedstocks, especially in laser powder bed fusion (LPBF) and electron beam melting (EBM), where powders are characterized by a D50V of 30 μm or greater. Along with variations in boundary conditions introduced by the layer-wise geometry and surface topography of the printed component, stochastic interactions between the spreading tool and powder result in spatial variations of layer quality that are still not well understood. Here, to study powder spreading under conditions representative of PBF AM, we employ a modular, mechanized apparatus to create powder layers from moderately and highly cohesive powders (nominally 15–45μm Ti–6Al–4V and 20–63μm Al–10Si–Mg, respectively) with a selection of blade-like spreading tools. Powder layer effective depth is spatially mapped using transmission X-ray imaging, and uniformity is quantified via a statistical approach. We first compare layer density, or the effective depth of powder layer, and show that blade geometries with a curved profile lead to increased material deposition. Second, this approach enables quantification of local fluctuations, or layer defect severity. For example, we observe that the primary benefit of a V-shaped rubber (compliant) blade, as compared to a 45° rigid blade, lies in enabling local deflection of the blade edge to eliminate streaking from large particles, while also increasing deposition (layer density). Additionally, we employ a custom DEM simulation to elucidate the opposing roles of particle density and surface energy with a pseudo-material approach, where the balance of inertial and cohesive forces determine macro-scale powder flowability. For each alloy density, selected to represent Ti–6Al–4V and Al–10Si–Mg, we find via simulations a critical surface energy beyond which layer density is greatly impaired when powder spreading is performed using a blade.

Quantitative analysis of thin metal powder layers via transmission X-ray imaging and discrete element simulation: Roller-based spreading approaches

A variety of tools can be used for spreading metal, ceramic, and polymer feedstocks in powder bed additive manufacturing (AM) methods. Rollers are often employed when spreading powders with limited flowability, as arises in powders comprising fine particle sizes or high surface energy materials. Here, we study roller-based powder spreading for powder bed AM using the unique combination of a purpose-built powder spreading testbed with a proven method for X-ray mapping of powder layer depth. We focus on the density and uniformity of nominally 100 μm thick layers of roller-spread Ti-6Al-4V and Al-10Si-Mg powders. Our results indicate that when rotation is too rapid, roller-applied forces including shear and medium fluid drag impede the creation of dense and uniform layers from powders of high innate flowability, or where inertial forces driven by particle density dominate cohesive forces. Roller counter-rotation augments the uniformity of cohesive powder layers, primarily though reducing the influence of particle clusters in the flowing powder, which are otherwise shown to cause deep, trench-like streaks. Companion discrete element method (DEM) simulations further contextualize the experiments through isolation of the effects of cohesion on layer attributes. Results suggest that roller motion parameters could apply a strategic level of additional shear force to the flowing powder, thereby mitigating the clumping behavior characteristic of highly cohesive feedstocks while maintaining high layer uniformity.

X-ray Detection of Prohibited Item Method Based on Dual Attention Mechanism

Prohibited item detection plays a significant role in ensuring public safety, as the timely and accurate identification of prohibited items ensures the safety of lives and property. X-ray transmission imaging technology is commonly employed for prohibited item detection in public spaces, producing X-ray images of luggage to visualize their internal contents. However, challenges such as multiple object overlapping, varying angles, loss of details, and small targets in X-ray transmission imaging pose significant obstacles to prohibited item detection. Therefore, a dual attention mechanism network (DAMN) for X-ray prohibited item detection is proposed. The DAMN consists of three modules, i.e., spatial attention, channel attention, and dependency relationship optimization. A long-range dependency model is achieved by employing a dual attention mechanism with spatial and channel attention, effectively extracting feature information. Meanwhile, the dependency relationship module is integrated to address the shortcomings of traditional convolutional networks in terms of short-range correlations. We conducted experiments comparing the DAMN with several existing algorithms on datasets containing 12 categories of prohibited items, including firearms and knives. The results show that the DAMN has a good performance, particularly in scenarios involving small object detection, detail loss, and target overlap under complex conditions. Specifically, the detection average precision of the DAMN reaches 63.8%, with a segmentation average precision of 54.7%.

Intercalation of Fe-montmorillonite for developing nacre-inspired flexible all-solid-state supercapacitor with circular economy approach

The concept of circular economy has gained attention for developing environmentally friendly and sustainable next-generation flexible supercapacitors. Montmorillonite (MMT) has been used to intercalate Fe ions (Fe-MMT) in designing an asymmetric flexible all-solid-state supercapacitor (FASC). The negative electrode was fabricated using PEDOT:PSS, and the positive nanocomposite electrode was made of Fe-MMT, PEDOT:PSS, and PVA, along with a gel electrolyte. Fe-MMT exhibited redox properties, allowing for the reaction of Fe2+/3+/Fe3+ during the charge-discharge cycle, as measured through in-situ X-ray absorption near-edge spectroscopy combined with cyclic-voltammetric measurements. The Fe-MMT nanocomposite exhibited a unique hierarchical nacre-like superstructure, characterized by synchrotron-based transmission X-ray imaging and X-ray diffraction, providing outstanding stability of the charge-discharge cycle under dynamic twisting and high-throughput channels to enhance charge diffusion. The maximum power density achieved was 2181 W kg−1 at 0.83 W h kg−1, and the maximum energy density was 3.27 W h kg−1 at 549 W kg−1. The active cations of the MMT-based electrodes could be further acquired from wastewater recycling, making this an eco-supercapacitor according to the concept of the circular economy. The use of MMT as a cation-exchange material demonstrates its potential for environmentally sustainable energy storage applications.

Stereo X-Ray Tomography

X-ray tomography is a powerful volumetric imaging technique, but detailed three dimensional (3D) imaging requires the acquisition of a large number of individual X-ray images, which is time consuming. For applications where spatial information needs to be collected quickly, for example, when studying dynamic processes, standard X-ray tomography is therefore not applicable. Inspired by stereo vision, in this paper, we develop X-ray imaging methods that work with two X-ray projection images. In this setting, without the use of additional strong prior information, we no longer have enough information to fully recover the 3D tomographic images. However, up to a point, we are nevertheless able to extract spatial locations of point and line features. From stereo vision, it is well known that, for a known imaging geometry, once the same point is identified in two images taken from different directions, then the point’s location in 3D space is exactly specified. The challenge is the matching of points between images. As X-ray transmission images are fundamentally different from the surface reflection images used in standard computer vision, we here develop a different feature identification and matching approach. In fact, once point like features are identified, if there are limited points in the image, then they can often be matched exactly. In fact, by utilising a third observation from an appropriate direction, matching becomes unique. Once matched, point locations in 3D space are easily computed using geometric considerations. Linear features, with clear end points, can be located using a similar approach.

Development of a data assimilation system for the investigation of the dendrite solidification process by integrating in situ X-ray imaging and phase-field simulation

The dendrite solidification process has been observed and simulated using state-of-the-art techniques, such as time-resolved X-ray tomography (4D-CT) and high-performance phase-field (PF) simulations. 4D-CT has enabled the direct observation of the 3D dendrite growth in opaque alloys. However, the spatiotemporal resolution is not sufficient for investigating fast phenomena because a 3D solidification structure is obtained using hundreds of transmission images during the 180° rotation of a sample. High-performance PF simulations have enabled the simulation of multiple 3D dendrite growth phenomena. However, the material properties required in PF solutions of alloys are often unavailable. Therefore, integrating in situ X-ray observations with PF simulations using data assimilation is a promising approach for simultaneously solving these issues. In this study, we developed a data assimilation system with an ensemble Kalman filter, in which the solid fraction along the thickness of a sample was used as observation data to enable data assimilation using X-ray transmission images. The performance of the developed data assimilation system was evaluated via twin experiments for columnar dendrite growth during the directional solidification of a binary alloy in a thin film. The results showed that data assimilation using the solid fraction as observation data estimated the material properties and solidification morphologies with reasonable accuracy.

Analysing operando 2D X-ray transmission images for liquid water distribution in polymer electrolyte fuel cells

Analysing operando 2D X-ray transmission images for liquid water distribution in polymer electrolyte fuel cells

Sub-resolution modeling of the apparent mass loss in quantitative broadband X-ray radiography

The quantitative impact of image blur on calculated object mass in X-ray radiography is explored. Radiographed object masses are initially estimated through experimental calibration and compared against their known true mass, with the deviation, or “apparent mass loss”. Apparent mass loss occurs due to the effect of spatial blur on X-ray transmission images that reduces the measured mass because while the image intensity is conserved with spatial blur, the nonlinear conversion to path length is not. A synthetic model is proposed where an object’s apparent mass loss is estimated through the amount of blur present within the radiograph image. The image blur is inferred through a combination of readily available image parameters related to the signal levels, object shape, and experimental extrinsics. A regularized regression model is built that correlates these blur parameters to the expected mass loss. The model is first trained on 2100 and tested on 140 synthetically created objects, and then later verified on two separate experimental setups of type 304 stainless steel objects imaged with a portable tube source at 150 kVp and type 6061 aluminum objects at 80 kVp. The proposed model allows for a simple post hoc reduction in errors of approximately 20% in object mass through X-ray radiography.

Design of a five-layers multi-energy X-ray imaging detector for material sorting

X-ray transmission imaging (XRT) is widely used for sorting materials. However, conventional single-energy and dual-energy X-ray systems have poor ability to discriminate between materials with similar atomic number (Z), and the count rate of available multi-energy XRT detectors could not support high-speed industrial applications. This paper presents the design of a detector that can potentiality achieve high-speed multi-energy X-ray imaging using Geant4 simulations. This detector consisted of five detection layers (with three scintillator materials: CsI, GOS and CdWO4), two metal filters, which allows X-ray imaging at five energies. Validation simulation showed that the 15% more accurate than a dual-layers detector in the classification of Mg and Al alloys.

Development of capacitance sensor for void fraction measurement in a packed bed of spheres

A capacitance void fraction sensor (CVS) is applied to measure the volumetric averaged void fraction in a packed bed of spheres. The void fraction in the packed bed is one of the most important parameters to evaluate cooling characteristics in a porous debris bed during a severe accident of nuclear reactors, and the quantitative void fraction measuring technique for such porous flow channels should be developed. The CVS is a very simple method, and the void fraction is estimated from the electrical capacitance measured between the electrodes installed on the pipe. Generally, the linear relationship or Maxwell equation could be applied to estimate the void fraction from the capacitance measured by the CVS. However, the electrical field in the packed bed becomes complex due to the existence of spheres. Therefore, they may not be applied to the void fraction estimation in the packed bed. In this study, the CVS with a ring-type electrode configuration is used for the sphere-packed beds, and the applicability of the CVS is investigated. At first, the particle size and the pipe diameter are varied in the packed test section, and X-ray transmission imaging is used to clarify the relation between the void fraction and the capacitance in the packed bed. Then, it is found that the void fraction can be obtained by the coefficient in Maxwell's equation, depending on the packed bed properties. Finally, the measurement accuracy of the CVS for the sphere-packed bed is estimated by comparing it with a volumetric method, and the availability of the proposed method is shown.

A Multienergy Computed Tomography Method Based on a Blind Decomposition Model for Multivoltage X-Ray Transmission Images

Multienergy computed tomography (CT) can be implemented based on traditional CT systems to reduce the hardware cost. However, prior knowledge of the X-ray spectra or material composition and multiple scans are required in most such implementation methods, which imposes an extra burden. To avoid this, a method based on a blind decomposition model of multivoltage X-ray transmission images is proposed in this paper. The model considers X-ray scattering as an important factor resulting in errors of theoretical and measured polychromatic X-ray transmission. Since X-ray scatter has low-frequency characteristics, it is estimated via local averaging of the errors and subtracted from the errors. The solution algorithm for the model is derived with the Karush–Kuhn–Tucker conditions. In addition, image segmentation of the projection or reconstructed image is not needed. Samples representing different imaging situations are used in the experiments. Beam hardening artifacts are obviously reduced in the reconstructed images obtained with the proposed method. The relative errors of the X-ray energies of different component materials are calculated for the reconstructed images. For the first sample, the errors are less than 1% in the reconstructed images obtained with the proposed method and are seen to be the best among all compared methods. For the second sample, the errors are less than 10% in most reconstructed images obtained with the proposed method, whereas they are larger than 10% in most reconstructed images obtained with the compared methods. These results demonstrate the effectiveness of the proposed method.

Dynamical X-ray phase imaging of polymer materials under laser engraving

X-ray phase imaging provides higher sensitivity to polymer materials made of light elements than the sensitivity provided by conventional X-ray transmission imaging that relies on absorption contrast. High-speed X-ray phase imaging has recently been studied using pink-beam synchrotron radiation and a Talbot interferometer to enable dynamic observation. In this study, we adopted high-speed X-ray phase imaging for the inner visualization of polymer materials under laser engraving. A sequence of phase-stepping-based phase imaging with a temporal resolution of 5 ms was established by synchronizing an X-ray detector with the linear motions of a grating and a sample. The laser and X-ray paths were aligned orthogonally, and the engraving region was observed from the material surface in the depth direction, in which ablation followed by bubble formation with a certain time delay was demonstrated.