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Quasi-two-dimensional (Q-2D) perovskite exhibits exceptional photoelectric properties and demonstrates reduced ion migration compared to 3D perovskite, making it a promising material for the fabrication of highly sensitive and stable X-ray detectors. However, achieving high-quality perovskite films with sufficient thickness for efficient X-ray absorption remains challenging. Herein, we present a novel approach to regulate the growth of Q-2D perovskite crystals in a mixed atmosphere comprising methylamine (CH 3 NH 2 , MA) and ammonia (NH 3 ), resulting in the successful fabrication of high-quality films with a thickness of hundreds of micrometers. Subsequently, we build a heterojunction X-ray detector by incorporating the perovskite layer with titanium dioxide (TiO 2 ). The precise regulation of perovskite crystal growth and the meticulous design of the device structure synergistically enhance the resistivity and carrier transport properties of the X-ray detector, resulting in an ultrahigh sensitivity (29721.4 μC Gy air -1 ) for low-dimensional perovskite X-ray detectors and a low detection limit of 20.9 nGy -2 ) for low-dimensional perovskite X-ray detectors and a low detection limit of 20.9 nGy air . We have further demonstrated a flat panel X-ray imager (FPXI) showing a high spatial resolution of 3.6 lp mm -1 and outstanding X-ray imaging capability under low X-ray doses. This work presents an effective methodology for achieving high-performance Q-2D perovskite FPXIs that holds great promise for various applications in imaging technology.-1 and outstanding X-ray imaging capability under low X-ray doses. This work presents an effective methodology for achieving high-performance Q-2D perovskite FPXIs that holds great promise for various applications in imaging technology., (© 2024. The Author(s).)

More than 200 million X-ray images are taken annually in the United States of America using several medical imaging procedures like conventional X-ray radiography, computed tomography (CT), fluoroscopy, and mammography. About 12% of mammograms result in false positives due to poor image resolution between dense breast tissues and cancerous tissues. False positive results lead to additional follow-up screening that cost billion s of dollars each year. This work aims to improve image resolution utilizing a novel direct conversion perovskite–graphene imaging detector that will enhance the X-ray detection sensitivity, which will improve the overall image quality compared to existing a-Se sensors installed in tomosynthesis and mammography units. A graphene field effect transistor perovskite sensor prototype made with metal halide perovskites was built and tested at Oregon State University. The sensor that was exposed to a micro-CT X-ray beam with 35 kVp, registered a sensitivity of 870 uC/cm2/R. The Planar design of the perovskite–graphene sensor was exposed to a 35 kVp and 50 kVp energy beams and registered a 500 uC/cm2/R and 350 uC/cm2/R sensitivity, respectively. The sensor sensitivity was found to be 3–4 orders of magnitude greater than current a-Se sensors used in mammography systems. Improvements in sensitivity should lead to better image quality, and thus reduce the percentage of false positive results, which will decrease the need for secondary screenings. [ABSTRACT FROM AUTHOR]

Highlights: The classification of 2D perovskite is summarized, and the preparation methods of 2D perovskite according to the requirements of X-ray detection materials are introduced. We analyzed the advantages and insufficiency of different devices and introduced improvement measures, including ion migration, charge transfer performance, stability, and 2D/3D heterojunctions. Finally, we introduced the potential preponderances of 2D perovskite in the scintillation detection field; meanwhile, the main challenges facing the practical application of 2D perovskite X-ray detectors are analyzed. Metal halide perovskites have recently emerged as promising candidates for the next generation of X-ray detectors due to their excellent optoelectronic properties. Especially, two-dimensional (2D) perovskites afford many distinct properties, including remarkable structural diversity, high generation energy, and balanced large exciton binding energy. With the advantages of 2D materials and perovskites, it successfully reduces the decomposition and phase transition of perovskite and effectively suppresses ion migration. Meanwhile, the existence of a high hydrophobic spacer can block water molecules, thus making 2D perovskite obtain excellent stability. All of these advantages have attracted much attention in the field of X-ray detection. This review introduces the classification of 2D halide perovskites, summarizes the synthesis technology and performance characteristics of 2D perovskite X-ray direct detector, and briefly discusses the application of 2D perovskite in scintillators. Finally, this review also emphasizes the key challenges faced by 2D perovskite X-ray detectors in practical application and presents our views on its future development. [ABSTRACT FROM AUTHOR]

Metal halide perovskites are becoming increasingly attractive for X-ray detection applications due to their distinctive optoelectronic properties. However, the practical use of lead-based organic–inorganic hybrid perovskites have been significantly impeded by their toxicity and instability. In this study, we have achieved successful growth lead-free bismuth-based all-inorganic perovskite Cs3Bi2Br9 films using a chemical vapor deposition (CVD) method for the first time. The CVD process has the advantage of circumventing the solubility limit of raw materials in conventional solution process, enabling thick film deposition for enhanced X-ray attenuation. Through meticulous adjustments to the molar ratio of the precursor materials, we have produced high-quality films characterized by a smooth surface and compact grains, ideal for minimizing leakage current. Consequently, the optimized devices exhibit an exceptionally low dark current density of 0.47 nA cm−2, meeting industrial standards for integration with thin-film transistor backplanes. Furthermore, these devices demonstrate an impressive sensitivity of 593 μC Gyair−1 cm−2, a low detection limit of 187.7 nGyair s−1, as well as excellent storage and operational stabilities, which hold great promise as an eco-friendly candidate for high-performance X-ray detection and imaging applications. [ABSTRACT FROM AUTHOR]

Inorganic perovskite wafers with good stability and adjustable sizes are promising in X-ray detection but the high synthetic temperature is a hindrance. Herein, dimethyl sulfoxide (DMSO) is used to prepare the CsPbBr3 micro-bricks powder at room temperature. The CsPbBr3 powder has a cubic shape with few crystal defects, small charge trap density, and high crystallinity. A trace amount of DMSO attaches to the surface of the CsPbBr3 micro-bricks via Pb-O bonding, forming the CsPbBr3-DMSO adduct. During hot isostatic processing, the released DMSO vapor merges the CsPbBr3 micro-bricks, producing a compact and dense CsPbBr3 wafer with minimized grain boundaries and excellent charge transport properties. The CsPbBr3 wafer shows a large mobility-lifetime (μτ) product of 5.16 × 10−4 cm2·V−1, high sensitivity of 14,430 μC·Gyair−1·cm−2, low detection limit of 564 nGyair·s−1, as well as robust stability in X-ray detection. The results reveal a novel strategy with immense practical potential pertaining to high-contrast X-ray detection. [ABSTRACT FROM AUTHOR]

Metal-halide perovskite thin monocrystals featuring efficient carrier collection and transport capabilities are well suited for radiation detectors, yet their growth in a generic, well-controlled manner remains challenging. Here, we reveal that mass transfer is one major limiting factor during solution growth of perovskite thin monocrystals. A general approach is developed to overcome synthetic limitation by using a high solute flux system, in which mass diffusion coefficient is improved from 1.7×10–10 to 5.4×10–10 m2 s–1 by suppressing monomer aggregation. The generality of this approach is validated by the synthesis of 29 types of perovskite thin monocrystals at 40–90 °C with the growth velocity up to 27.2 μm min–1. The as-grown perovskite monocrystals deliver a high X-ray sensitivity of 1.74×105 µC Gy−1 cm−2 without applied bias. The findings regarding limited mass transfer and high-flux crystallization are crucial towards advancing the preparation and application of perovskite thin monocrystals. Liu et al. report a universal solution growth method for perovskite thin monocrystals by improving the mass transfer in the high solute flux system. The approach is applied to 29 types of perovskites with growth velocity up to 27.2 µm min-1 and enables efficient self-driven X-ray detectors. [ABSTRACT FROM AUTHOR]

Halide perovskites have shown promising potential for direct X-ray detection due to their high X-ray absorption coefficient, low trap states and convenient fabrication process. However, it is still a challenge to achieve high sensitivity, low dark current and low detection limit in a single material. The deep reason for this is the trade-off between the material's μτ product and resistivity. Here we report the construction of an organic–inorganic hybrid anti-perovskite ((2-Habch)3Cl(PtI6)) with indirect transition and low orbital symmetry at the band edge to achieve an ultralong intrinsic lifetime and thus break the trade-off. (2-Habch)3Cl(PtI6) achieves an unprecedented long carrier lifetime of >3 ms, leading to a large μτ product of 6.25 × 10−3 cm2 V−1 and high resistivity of 1012 Ω cm, outperforming most X-ray detection materials. These properties enabled the development of X-ray detectors that simultaneously achieve an ultralow dark current of 0.21 nA cm−2, high sensitivity of 1.0 × 104 µC Gyair−1 cm−2, ultralow detection limit of 2.4 nGyair s−1 and excellent operational stability with no observable baseline drift, outperforming state-of-the-art perovskite single-crystal detectors. The rare combination of high performance in almost every figure of merit in the anti-perovskite-based X-ray detector could enable new-generation X-ray detection systems. The researchers synthesize organic–inorganic hybrid inverse perovskites that exhibit excellent carrier lifetime and mobility–lifetime product and high resistivity, enabling stable X-ray detectors with performance arguably outperforming state-of-the-art perovskite single-crystal detectors. [ABSTRACT FROM AUTHOR]

The emerging pseudohalide metal-free perovskite (pseudohalide–MFPs) X-ray detector caters to the demands of timely mobile diagnosis owing to its lightweight, flexibility, and cost-effectiveness. However, the performance of these devices is severely limited by poor X-ray absorption, ultra-wide band gap, relative instability, and their unknown biotoxicity. Herein, we construct heavy atom covalent bonds (C–Br/Cl) on the A-site organic cation to reinforce component coordination to modulate X-ray absorption and band gap in pseudohalide–MFPs and further enhance its stability. Molecular dynamics simulations demonstrate that the introduction of halogen atoms can strengthen hydrogen bonding interactions, thereby improving the coordination between different components. The resultant (MDABCOBr)–NH4(BF4)3 (MDABCO = N-methyl-N'-diazabicyclo[2.2.2]octonium) single crystal significantly increases X-ray absorption cross-section and crystalline density (from 1.728 to 1.950 g cm−3), and synergistically realizes the band nature modulation (from 7.4 to 5.5 eV) and enhanced ionic migration inhibition (628 meV) with optimized stability. As such, our X-ray detectors realized a sensitivity of 2377 μC Gyair−1 cm−2, an ultralow detection limit of 50.1 nGyair s−1, and impressive operation stability. Moreover, cytotoxicity assay confirmed the compatibility of pseudohalide metal-free perovskite. Finally, within this framework, we successfully fabricate the (MDABCOBr)–NH4(BF4)3-based flexible device to create an ideal in vitro wearable X-ray detection. [ABSTRACT FROM AUTHOR]

We report the photon (PL), electron (CL) and X-ray (XEL) induced luminescence characteristics of high aspect ratio ultra-long (~ 50 µm) ZnO nanorods (NRs) and discuss the potential for fast X-ray detection based on the consistent and efficient visible emission (~ 580 nm) from ZnO NRs. Nanostructured ZnO scintillators were rearranged to form a vertically well-aligned NR design in order to help light absorption and coupling resulting in luminescent and fast scintillation properties. The design of the nanorod array combines the key advantages of a low-cost growth technique together with environmentally friendly and widely available materials. A low temperature hydrothermal method was adopted to grow ZnO NRs in one cycle growth and their structural, optical and X-ray scintillation properties were investigated. The relatively short (~ 10 µm) ZnO NRs emitting in the near-band-edge region were found to be almost insensitive to X-rays. On the other hand, the higher XEL response of long ZnO NRs, which is a key parameter for evaluation of materials to be used as scintillators for high quality X-ray detection and imaging, along with a decay time response in the order of ns confirmed promising scintillation properties for fast and high-resolution X-ray detector applications. [ABSTRACT FROM AUTHOR]

Transition-edge sensors (TESs) are used as very sensitive thermometers in microcalorimeters aimed at detection of different wavelengths. In particular, for soft X-ray astrophysics, science goals require very high-resolution microcalorimeters which can be achieved with TESs coupled to suitable absorbers. For many applications, there is also need for a high number of pixels which typically requires multiplexing in the readout stage. Frequency-domain multiplexing (FDM) is a common scheme and is the baseline proposed for the ATHENA mission. FDM requires biasing the TES in AC at MHz frequencies. Recently, there has been reported degradation in performances under AC with respect to DC bias. In order to assess the performances of TESs to be used with FDM, it is thus of great interest to compare the performances of the same device both under AC bias and DC bias. This requires two different measurement set-ups with different processes for making the characterization. We report in this work the preliminary results of a single-pixel characterization performed on a TiAu TES under AC and afterwards under DC bias in different facilities. Extraction of dynamical parameters and noise performances are compared in both cases as a first stage for further AC/DC comparison of these devices. [ABSTRACT FROM AUTHOR]

X-ray detection is widely used in various applications. However, to meet the demand for high image quality and high accuracy diagnosis, the raw data increases and imposes challenges for conventional X-ray detection hardware regarding data transmission and power consumption. To tackle these issues, we present a scheme of in-X-ray-detector computing based on CsPbBr3 single-crystal detector with convenient polarity reconfigurability, good linear dynamic range, and robust stability. The detector features a stable trap-free device structure and achieves a high linear dynamic range of 106 dB. As a result, the detector could achieve edge extraction imaging with a data compression ratio of ~50%, and could also be programmed and trained to perform pattern recognition tasks with a high accuracy of 100%. Our research shows that in-X-ray-detector computing can be used in flexible and complex scenarios, making it a promising platform for intelligent X-ray imaging. In-sensor computing requires detectors with polarity reconfigurability and linear responsivity. Pang et al. report a CsPbBr3 perovskite single crystal X-ray detector for edge extraction imaging with a data compression ratio of 46.4% and classification task with an accuracy of 100%. [ABSTRACT FROM AUTHOR]

The current challenge of wearable/implantable personal dosimeters for medical diagnosis and radiotherapy applications is lack of suitable detector materials possessing both excellent detection performance and biocompatibility. Here, we report a solution-grown biocompatible organic single crystalline semiconductor (OSCS), 4-Hydroxyphenylacetic acid (4HPA), achieving real-time spectral detection of charged particles with single-particle sensitivity. Along in-plane direction, two-dimensional anisotropic 4HPA exhibits a large electron drift velocity of 5 × 105 cm s−1 at "radiation-mode" while maintaining a high resistivity of (1.28 ± 0.003) × 1012 Ω·cm at "dark-mode" due to influence of dense π-π overlaps and high-energy L1 level. Therefore, 4HPA detectors exhibit the record spectra detection of charged particles among their organic counterparts, with energy resolution of 36%, (μt)e of (4.91 ± 0.07) × 10−5 cm2 V−1, and detection time down to 3 ms. These detectors also show high X-ray detection sensitivity of 16,612 μC Gyabs−1 cm−3, detection of limit of 20 nGyair s−1, and long-term stability after 690 Gyair irradiation. New detector materials are crucial for radiation beam monitoring in dosimeters and X-ray imaging. The authors report a solution-grown biocompatible organic single crystalline semiconductor for real-time spectral detection of charged particles with single-particle sensitivity, X-ray detection and imaging. [ABSTRACT FROM AUTHOR]

Semiconductive metal–organic frameworks (MOFs) have attracted great interest for the electronic applications. However, dark currents of present hybrid organic–inorganic materials are 1000–10,000 times higher than those of commercial inorganic detectors, leading to poor charge transportation. Here, we demonstrate a ZIF-8 (Zn(mim)2, mim = 2-methylimidazolate) wafer with ultra-low dark current of 1.27 pA·mm−2 under high electric fields of 322 V·mm−1. The isostatic pressing preparation process provides ZIF-8 wafers with good transmittance. Besides, the presence of redox-active metals and small spatial separation between components promotes the charge hopping. The ZIF-8-based semiconductor detector shows promising X-ray detection sensitivity of 70.82 µC·Gy−1·cm−2 with low doses exposures, contributing to superior X-ray imaging capability with a relatively high spatial resolution of 1.2 Ip·mm−1. Simultaneously, good peak discrimination with the energy resolution of ∼ 43.78% is disclosed when the detector is illuminated by uncollimated 241Am@5.48 MeV α-particles. These results provide a broad prospect of MOFs for future radiation detection applications. [ABSTRACT FROM AUTHOR]

This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray images alone, consultations with practicing radiologists indicate that clinical data is highly informative and essential for interpreting medical images and making proper diagnoses. In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data). Since these data modalities are in different dimensional spaces, we propose a spatial arrangement strategy, spatialization, to facilitate the multimodal learning process in a Mask R-CNN model. We performed an extensive experimental evaluation using MIMIC-Eye, a dataset comprising different modalities: MIMIC-CXR (chest X-ray images), MIMIC IV-ED (patients' clinical data), and REFLACX (annotations of disease locations in chest X-rays). Results show that incorporating patients' clinical data in a DL model together with the proposed fusion methods improves the disease localization in chest X-rays by 12% in terms of Average Precision compared to a standard Mask R-CNN using chest X-rays alone. Further ablation studies also emphasize the importance of multimodal DL architectures and the incorporation of patients' clinical data in disease localization. In the interest of fostering scientific reproducibility, the architecture proposed within this investigation has been made publicly accessible(https://github.com/ChihchengHsieh/multimodal-abnormalities-detection). [ABSTRACT FROM AUTHOR]

Our study focuses on analysing the coronal, transition and chromospheric activity of four rapidly rotating stars located within 50 pc in the solar neighborhood. We have used the multi-wavelength capabilities of AstroSat to investigate the outer atmospheres of AB Dor, BO Mic, DG CVn and GJ 3331. These stars, classified as M and K type active stars, are known for their short rotation periods, leading to increased surface magnetic activity. Our soft X-ray observations provide the coronal properties, such as emission measures, temperatures and elemental coronal abundances. We reported the detection of X-ray flares from AB Dor, BO Mic and DG CVn, while UV light curves reveal flares in both BO Mic and DG CVn. [ABSTRACT FROM AUTHOR]

We synthesized HfxSi1−xO2 microparticle-loaded poly(9-vinylcarbazole) (PVK)-based plastic scintillators using the sol–gel method for high-energy X-ray detection at a high counting rate. We successfully dispersed HfxSi1−xO2 microparticles into PVK-based plastic scintillators using hafnium dichloride oxide octahydrate and phenyltrimethoxysilane at Hf concentrations of up to 20 wt%. The scintillation decay time constants of the first component were approximately 2.0 ns. Loading HfxSi1−xO2 microparticles enhanced the detection efficiency for 67.41 keV X-rays. The detection efficiency per 1 mm thickness and light yield of the Hf-12.5 wt%-loaded plastic scintillator were, respectively, 2.0 and 1.1 times higher than those of EJ-256, a commercial Pb-5 wt%-loaded plastic scintillator. The decrease in light yield was successfully suppressed; the light yield was reduced by 44% even at a Hf concentration of 12.5 wt%. We successfully enhanced the high-energy X-ray detection characteristics of PVK-based plastic scintillators using the sol–gel method. [ABSTRACT FROM AUTHOR]

Challenges associated with stretchable optoelectronic devices, such as pixel size, power consumption and stability, severely brock their realization in high-resolution digital imaging. Herein, we develop a universal detachable interface technique that allows uniform, damage-free and reproducible integration of micropatterned stretchable electrodes for pixel-dense intrinsically stretchable organic transistor arrays. Benefiting from the ideal heterocontact and short channel length (2 μm) in our transistors, switching current ratio exceeding 106, device density of 41,000 transistors/cm2, operational voltage down to 5 V and excellent stability are simultaneously achieved. The resultant stretchable transistor-based image sensors exhibit ultrasensitive X-ray detection and high-resolution imaging capability. A megapixel image is demonstrated, which is unprecedented for stretchable direct-conversion X-ray detectors. These results forge a bright future for the stretchable photonic integration toward next-generation visualization equipment. Pixel size, power consumption, and stability of stretchable optoelectronic devices limit their application in digital imaging. Bian et al. developed a universal detachable interface technique for damage-free micropattern, reproducible transfer and ideal heterocontact of intrinsically stretchable electrodes. [ABSTRACT FROM AUTHOR]

We report on the structural, photoluminescence (PL), and scintillation properties of Ce-doped P2O5–Al2O3–Cs2O–BaO glass scintillators having different Ce concentrations (1.96, 3.85, 7.41, 10.7, and 13.8 mol%) for gamma- and X-ray detection. The highest density (3.57 g/cm3) was achieved with Ce concentration of 13.8 mol%. The PL emission and X-ray-induced radioluminescence spectra showed emission peaks corresponding to the allowed transition from the Ce3+ 5d excited state to the 4f ground states. The highest scintillation light yields (approximately 2200 photons/MeV) were achieved with Ce concentrations of 7.41, 10.7, and 13.8 mol%. These light yields are higher than those previously reported for glasses containing heavy elements, such as Eu-doped SiO2–Al2O3–HfO2 (630 photons/MeV), Ce-doped P2O5–Li2O–GdI3–Al2O3–CaCO3 (1600 photons/MeV), and Ce-doped SiO2–Al2O3–BaF2–Gd2O3 (2050 photons/MeV). For all glasses, short scintillation decay times (28.5–34.0 ns) corresponding to the transition from the Ce3+ 5d excited state to the 4f ground states were observed. These results indicate that the fabricated glass with an optimal Ce concentration of 13.8 mol% is suitable for gamma- and X-ray detection. [ABSTRACT FROM AUTHOR]

Within the scope of this investigation, we carried out experiments to investigate the potential of the Vision Transformer (ViT) in the field of medical image analysis. The diagnosis of osteoporosis through inspection of X-ray radio-images is a substantial classification problem that we were able to address with the assistance of Vision Transformer models. In order to provide a basis for comparison, we conducted a parallel analysis in which we sought to solve the same problem by employing traditional convolutional neural networks (CNNs), which are well-known and commonly used techniques for the solution of image categorization issues. The findings of our research led us to conclude that ViT is capable of achieving superior outcomes compared to CNN. Furthermore, provided that methods have access to a sufficient quantity of training data, the probability increases that both methods arrive at more appropriate solutions to critical issues. [ABSTRACT FROM AUTHOR]

Edge detection is an essential computer vision phenomenon that is useful for contour detection and in turn it is useful for acquiring vital information. There are a variety of images, including medical, satellite, industrial and general-purpose images. An X-ray is a form of medical imaging that is created by transmitting electronic radiation into a patient's body to get an image of the human body's internal structures. This allows orthopedic surgeons and radiologists to make more accurate diagnoses of illness. Due to its versatility, soft computing approaches are regarded as effective edge detection tools. This study provides a novel fuzzy logic-based technique for edge detection in which the quality of edges is governed by a sharpening-guided filter. The proposed model holds four different patterns for extracting the edges from an image namely 2 × 2 mask, 3 × 2 mask, 2 × 3 mask and 3 × 3 mask. These patterns of masks target the edge pixels of an image along with the other pixels. The proposed model provides an enhanced approach for detecting edges in X-ray pictures of humans. Various statistical measurements are used to evaluate the proposed model to prove its significance. By adjusting the smoothing parameters appropriately, it has been discovered that the detected edges are significantly enhanced. The proposed model is implemented and evaluated under four different metrics namely Correlation Coefficient, Figure of Merit, Structure Similarity Image Metrics and Euclidian Distance. The proposed model results are compared five different state-of-the-art algorithms to prove the significance, and the result shows that the proposed model identifies the edge in a well-structured manner. [ABSTRACT FROM AUTHOR]

X-ray detection limit and sensitivity are important figure of merits for perovskite X-ray detectors, but literatures lack a valid mathematic expression for determining the lower limit of detection for a perovskite X-ray detector. In this work, we present a thorough analysis and new method for X-ray detection limit determination based on a statistical model that correlates the dark current and the X-ray induced photocurrent with the detection limit. The detection limit can be calculated through the measurement of dark current and sensitivity with an easy-to-follow practice. Alternatively, the detection limit may also be obtained by the measurement of dark current and photocurrent when repeatedly lowering the X-ray dose rate. While the material quality is critical, we show that the device architecture and working mode also have a significant influence on the sensitivity and the detection limit. Our work establishes a fair comparison metrics for material and detector development. The limit of X-ray detection is an important figure of merit for X-ray detectors, yet the suitability of method adopted from Currie's 1968 paper and the following international standard is in doubt. Here, the authors propose a statistical model that correlates dark current and photo-current, show how it can be used to determine detection limit. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) are a class of fascinating supramolecular crystalline materials that have been widely developed for catalysis, gas storage, illumination, drug delivery/cytoprotection, and so on. Recently, MOFs have been found to have potential applications in X-ray detection due to their high sensitivity, fast response time, high absorption coefficient, and radiation stability. In this review, we present an overview on the fundamental mechanism of using MOFs for X-ray scintillation. We further discuss the recent developments in X-ray detection based on indirect X-ray scintillation and direct X-ray conversion. Finally, we provide a summary and a perspective on the future of this promising research field. [ABSTRACT FROM AUTHOR]

Objective: Although there is growing evidence that ultrasonography is superior to X-ray for rib fractures' detection, X-ray is still indicated as the most appropriate method. This has partially been attributed to a lack of studies using an appropriate reference modality. We aimed to compare the diagnostic accuracy of ultrasonography and X-ray in the detection of rib fractures, considering CT as the reference standard. Materials and methods: Within a 2.5-year period, all consecutive patients with clinically suspected rib fracture(s) following blunt chest trauma and available posteroanterior/anteroposterior X-ray and thoracic CT were prospectively studied and planned to undergo thoracic ultrasonography, by a single operator. All imaging examinations were evaluated for cortical rib fracture(s), and their location was recorded. The cartilaginous rib portions were not assessed. CTs and X-rays were evaluated retrospectively. Concomitant thoracic/extra-thoracic injuries were assessed on CT. Comparisons were performed with the Mann–Whitney U test and Fisher's exact test. Results: Fifty-nine patients (32 males, 27 females; mean age, 53.1 ± 16.6 years) were included. CT, ultrasonography, and X-ray (40 posteroanterior/19 anteroposterior views) diagnosed 136/122/42 rib fractures in 56/54/27 patients, respectively. Ultrasonography and X-ray had sensitivity of 100%/40% and specificity of 89.7%/30.9% for rib fractures' detection. Ultrasound accuracy was 94.9% compared to 35.4% for X-rays (P <.001) in detecting individual rib fractures. Most fractures involved the 4th–9th ribs. Upper rib fractures were most commonly overlooked on ultrasonography. Thoracic cage/spine fractures and haemothorax represented the most common concomitant injuries. Conclusion: Ultrasonography appeared to be superior to X-ray for the detection of rib fractures with regard to a reference CT. [ABSTRACT FROM AUTHOR]

Human bone is an essential structure that allows the body to move. It is a common observation in contemporary society that bone fractures occur frequently. The doctors use X-rays, Computed Tomography scans, and Magnetic Resonance Imaging to determine the location of the broken bone. The previous method of evaluating broken bones in person was inefficient, often leading to errors. However, introducing new, advanced evaluation techniques has obliterated these issues. Consequently, it is essential to develop an automated system for identifying fractured bones. This research uses a new deep-learning model named "You Only Look Once (version 8)" to distinguish between healthy and broken bones from multi-modal images. We utilized a customized dataset named "Human Bone Fractures Multi-modal Image Dataset", which includes 641 images representing ten different classes of bone fractures. The small data set leads to an over-fitting of the model. To increase the amount of data, we utilized a data augmentation technique. Three experiments were conducted to assess the effectiveness of the model. The findings of the experiments show that the proposed study effectively identifies and classifies different types of fractures in this area. Our system attained 95% precision, 93% recall, and 92% of mean average precision. The outcomes demonstrated that the method achieves cutting-edge performance. [ABSTRACT FROM AUTHOR]

Experiments on the dynamic isentropic compression of solid CO2 samples by the megabar pressure induced by the superstrong magnetic field of an explosive magnetic generator have been performed with the X-ray diffraction detection of the state of the compressed samples. The generator operation is based on the fast compression of the initial magnetic flux in the cavity of the generator by a conducting cylindrical liner accelerated by the products of the explosion of a cylindrical explosive charge. Two points at pressures of 349 and 459 GPa on the compressibility diagram of CO2 have been determined in the experiments, where the degree of compression ρ/ρ0 of CO2 has reached currently highest values of 3.90 and 4.02, respectively. Comparison has shown that theoretically calculated equations of states for crystal phases of CO2 almost completely reproduce the experimental results, which confirms a high accuracy of theoretical predictions and the identity of the experimental and theoretical equations of states of CO2 modifications stable at high pressures. [ABSTRACT FROM AUTHOR]

The density of shock-compressed liquid krypton ρ ≈ 9 g/cm3 and the temperature T ≈ 55 000 K on the main Hugoniot adiabat in the pressure range of Р ≈ 140–255 GPa have been measured with hemispherical shock wave generators. Using spherical devices, gaseous krypton has been compressed to the density ρ ≈ 20 g/cm3 by the pressure Р ≈ 2700 GPa at the temperature T ≈ 120 000 K. The experiment has been carried out at the X-ray diffraction complex RGK-B-L (Russian Federal Nuclear Center All-Russian Scientific Research Institute of Experimental Physics) consisting of BIM 234.3000 betatrons with a boundary energy of 60 MeV used in the multipulse bremsstrahlung generation regime with a multichannel optoelectronic detection system for X-ray images. The designs of the experimental devices have been described, and the thermodynamic parameters of the krypton plasma reached in these devices have been estimated. The obtained data have been analyzed and compared to previously reported data. The compression of krypton to the density ρ ≈ 20 g/cm3 by the pressure Р ≈ 2700 GPa is currently a record achievement. [ABSTRACT FROM AUTHOR]

Objectives: Algorithms for fracture detection are spreading in clinical practice, but the use of X-ray-only ground truth can induce bias in their evaluation. This study assessed radiologists' performances to detect wrist and hand fractures on radiographs, using a commercially-available algorithm, compared to a computerized tomography (CT) ground truth. Methods: Post-traumatic hand and wrist CT and concomitant X-ray examinations were retrospectively gathered. Radiographs were labeled based on CT findings. The dataset was composed of 296 consecutive cases: 118 normal (39.9%), 178 pathological (60.1%) with a total of 267 fractures visible in CT. Twenty-three radiologists with various levels of experience reviewed all radiographs without AI, then using it, blinded towards CT results. Results: Using AI improved radiologists' sensitivity (Se, 0.658 to 0.703, p < 0.0001) and negative predictive value (NPV, 0.585 to 0.618, p < 0.0001), without affecting their specificity (Sp, 0.885 vs 0.891, p = 0.91) or positive predictive value (PPV, 0.887 vs 0.899, p = 0.08). On the radiographic dataset, based on the CT ground truth, stand-alone AI performances were 0.771 (Se), 0.898 (Sp), 0.684 (NPV), 0.915 (PPV), and 0.764 (AUROC) which were lower than previously reported, suggesting a potential underestimation of the number of missed fractures in the AI literature. Conclusions: AI enabled radiologists to improve their sensitivity and negative predictive value for wrist and hand fracture detection on radiographs, without affecting their specificity or positive predictive value, compared to a CT-based ground truth. Using CT as gold standard for X-ray labels is innovative, leading to algorithm performance poorer than reported elsewhere, but probably closer to clinical reality. Clinical relevance statement: Using an AI algorithm significantly improved radiologists' sensitivity and negative predictive value in detecting wrist and hand fractures on radiographs, with ground truth labels based on CT findings. Key Points: • Using CT as a ground truth for labeling X-rays is new in AI literature, and led to algorithm performance significantly poorer than reported elsewhere (AUROC: 0.764), but probably closer to clinical reality. • AI enabled radiologists to significantly improve their sensitivity (+ 4.5%) and negative predictive value (+ 3.3%) for the detection of wrist and hand fractures on X-rays. • There was no significant change in terms of specificity or positive predictive value. [ABSTRACT FROM AUTHOR]

COVID-19 has expanded overall across the globe after its initial cases were discovered in December 2019 in Wuhan—China. Because the virus has impacted people's health worldwide, its fast identification is essential for preventing disease spread and reducing mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is the primary leading method for detecting COVID-19 disease; it has high costs and long turnaround times. Hence, quick and easy-to-use innovative diagnostic instruments are required. According to a new study, COVID-19 is linked to discoveries in chest X-ray pictures. The suggested approach includes a stage of pre-processing with lung segmentation, removing the surroundings that do not provide information pertinent to the task and may result in biased results. The InceptionV3 and U-Net deep learning models used in this work process the X-ray photo and classifies them as COVID-19 negative or positive. The CNN model that uses a transfer learning approach was trained. Finally, the findings are analyzed and interpreted through different examples. The obtained COVID-19 detection accuracy is around 99% for the best models. [ABSTRACT FROM AUTHOR]

The organic-inorganic hybrid lead halide perovskites have emerged as a series of star materials for solar cells, lasers and detectors. However, the issues raised by the toxic lead element and marginal stability due to the volatile organic components have severely limited their potential applications. In this work, we develop a nucleation-controlled solution method to grow large size high-quality Cs3Bi2I9 perovskite single crystals (PSCs). Using the technique, we harvest some centimeter-sized single crystals and achieved high device performance. We find that X-ray detectors based on PSCs exhibit high sensitivity of 1652.3 μC Gyair−1 cm−2 and very low detectable dose rate of 130 nGyair s−1, both desired in medical diagnostics. In addition, its outstanding thermal stability inspires us to develop a high temperature X-ray detector with stable response at up to 100 °C. Furthermore, PSCs exhibit high X-ray imaging capability thanks to its negligible signal drifting and extremely high stability. Organic-inorganic hybrid halide perovskites have been emerged as promising candidates for X-ray detectors while toxicity and instability are known issues. Here, Zhang et al. grow large size lead-free single crystals for high performance X-ray detection and imaging devices that can operate at 100 °C. [ABSTRACT FROM AUTHOR]

The high-energy X-ray detection capabilities of hafnium oxide-doped organosilica plastic scintillators were analysed. Plastic scintillators with different concentrations of Hf were synthesized via the sol–gel method. In the photoluminescence emission spectra and X-ray-excited radioluminescence spectra, all of the plastic samples showed broad emission bands peaking between 350 and 500 nm derived from the fluorescence of the organic phosphor b-PBD. The maximum detection efficiency for a thickness of 1 mm towards 67.41 keV high-energy X-ray was estimated to be 6.3% (Hf 10 wt%) which is 3.7 times larger than that of the undoped sample and 1.9 times larger than that of the commercialized Pb 5 wt%-doped plastic scintillator EJ-256. The full width at half maximum time resolutions were estimated to be approximately 0.4 to 0.6 ns, which is almost independent of the Hf concentration of Hf. Therefore, the detection efficiency towards high-energy X-rays was successfully improved while maintaining the original high time resolution of plastic scintillators by adding Hf to the plastic scintillator at high concentrations. [ABSTRACT FROM AUTHOR]

Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results. [ABSTRACT FROM AUTHOR]

Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images. [ABSTRACT FROM AUTHOR]

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%. [ABSTRACT FROM AUTHOR]

Objectives: Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools. Methods: Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports. Results: Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001). Conclusion: The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future. Critical relevance statement: With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest. Key points: • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow. [ABSTRACT FROM AUTHOR]

Given that defect detection in weld X-ray images is a critical aspect of pressure vessel manufacturing and inspection, accurate differentiation of the type, distribution, number, and area of defects in the images serves as the foundation for judging weld quality, and the segmentation method of defects in digital X-ray images is the core technology for differentiating defects. Based on the publicly available weld seam dataset GDX-ray, this paper proposes a complete technique for fault segmentation in X-ray pictures of pressure vessel welds. The key works are as follows: (1) To address the problem of a lack of defect samples and imbalanced distribution inside GDX-ray, a DA-DCGAN based on a two-channel attention mechanism is devised to increase sample data. (2) A convolutional block attention mechanism is incorporated into the coding layer to boost the accuracy of small-scale defect identification. The proposed MAU-Net defect semantic segmentation network uses multi-scale even convolution to enhance large-scale features. The proposed method can mask electrostatic interference and non-defect-class parts in the actual weld X-ray images, achieve an average segmentation accuracy of 84.75% for the GDX-ray dataset, segment and accurately rate the valid defects with a correct rating rate of 95%, and thus realize practical value in engineering. [ABSTRACT FROM AUTHOR]

Mn-doped UO2 is under consideration for use as an accident tolerant nuclear fuel. We detail the synthesis of Mn-doped UO2 prepared via a wet co-precipitation method, which was refined to improve the yield of incorporated Mn. To verify the Mn-doped UO2 defect chemistry, X-ray absorption spectroscopy at the Mn K-edge was performed, in addition to X-ray diffraction, Raman spectroscopy and high-energy resolved fluorescence detection X-ray absorption near edge spectroscopy at the U M4-edge. It was established that Mn2+ directly substitutes for U4+ in the UO2 lattice, accompanied by oxygen vacancy (Ov) charge compensation. In contrast to other divalent-element doped UO2 materials, compelling evidence for U5+ in a charge compensating role was not found. This work furthers understanding of the structure and crystal chemistry of Mn-doped UO2, which could show potential advantages as a novel efficient advanced nuclear fuel. [ABSTRACT FROM AUTHOR]

Prohibited item detection from X-ray images determines whether any prohibited items are present in baggage, and great progress has recently been made in this field with the development of deep learning. Nevertheless, the appearance of an occluded item interacts with the cover, which is different from occlusions encountered in conventional object detection. We design three mechanisms to handle this challenge on the assumption that the occluded part is still partially observed. First, we propose a scale interaction module in which the features in neighboring scales interact one or more times to enhance the model's perception ability. Then, we design a cross-image weakly supervised semantic analysis model utilizing the coattention mechanism to perceive similar and different targets, breaking through the information bottleneck of the isolated detection of a single image. Finally, we introduce a multitask learning module to simultaneously optimize the model at the global level and pixel level. We evaluate our approach on the publicly available security inspection X-ray (SIXray) dataset, the occluded prohibited items X-ray (OPIXray) dataset, and the HIXray dataset, and the results show that our approach is competitive with other X-ray baggage inspection approaches. [ABSTRACT FROM AUTHOR]

The computer-aided diagnosis (CAD) for chest X-rays was developed more than 50 years ago. However, there are still unmet needs for its versatile use in our medical fields. We planned this study to develop a multipotent CAD model suitable for general use including in primary care areas. We planned this study to solve the problem by using computed tomography (CT) scan with its one-to-one matched chest X-ray dataset. The data was extracted and preprocessed by pulmonology experts by using the bounding boxes to locate lesions of interest. For detecting multiple lesions, multi-object detection by faster R-CNN and by RetinaNet was adopted and compared. A total of twelve diagnostic labels were defined as the followings: pleural effusion, atelectasis, pulmonary nodule, cardiomegaly, consolidation, emphysema, pneumothorax, chemo-port, bronchial wall thickening, reticular opacity, pleural thickening, and bronchiectasis. The Faster R-CNN model showed higher overall sensitivity than RetinaNet, nevertheless the values of specificity were opposite. Some values such as cardiomegaly and chemo-port showed excellent sensitivity (100.0%, both). Others showed that the unique results such as bronchial wall thickening, reticular opacity, and pleural thickening can be described in the chest area. As far as we know, this is the first study to develop an object detection model for chest X-rays based on chest area defined by CT scans in one-to-one matched manner, preprocessed and conducted by a group of experts in pulmonology. Our model can be a potential tool for detecting the whole chest area with multiple diagnoses from a simple X-ray that is routinely taken in most clinics and hospitals on daily basis. [ABSTRACT FROM AUTHOR]

X-ray detection is of great significance in biomedical, nondestructive, and scientific research. Lead halide perovskites have recently emerged as one of the most promising materials for direct X-ray detection. However, the lead toxicity remains a worrisome concern for further commercial application. Great efforts have been made to search for lead-free perovskites with similar optoelectronic properties. Here, we present a lead-free oxide double perovskite material Ba2AgIO6 for X-ray detection. The lead-free, all-inorganic nature, as well as the high density of Ba2AgIO6, promises excellent prospects in X-ray applications. By employing the hydrothermal method, we successfully synthesized highly crystalline Ba2AgIO6 powder with pure phase. Furthermore, we prepared Ba2AgIO6 wafers through isostatic pressure and built X-ray detectors with Au/Ba2AgIO6 wafer/Au photo-conductive structure. The as-prepared X-ray detectors showed a sensitivity of 18.9 µC/(Gyair·cm2) at 5 V/mm, similar to commercial α-Se detectors showcasing their advantages for X-ray detection. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "Novel fuzzy logic expert system-based edge detection for X-ray images" published in the journal Soft Computing. The publisher has retracted the article due to concerns about compromised editorial handling and peer review processes, as well as inappropriate references and being out of scope for the journal. The authors of the article disagree with the retraction. The retraction note provides contact information for the authors and their affiliations. [Extracted from the article]

Metal halide perovskites have fascinated the research community over the past decade, and demonstrated unprecedented success in optoelectronics. In particular, perovskite single crystals have emerged as promising candidates for ionization radiation detection, due to the excellent opto-electronic properties. However, most of the reported crystals are grown in organic solvents and require high temperature. In this work, we develop a low-temperature crystallization strategy to grow CsPbBr3 perovskite single crystals in water. Then, we carefully investigate the structure and optoelectronic properties of the crystals obtained, and compare them with CsPbBr3 crystals grown in dimethyl sulfoxide. Interestingly, the water grown crystals exhibit a distinct crystal habit, superior charge transport properties and better stability in air. We also fabricate X-ray detectors based on the CsPbBr3 crystals, and systematically characterize their device performance. The crystals grown in water demonstrate great potential for X-ray imaging with enhanced performance metrics. Perovskite single crystals are commonly grown in organic solvents, which require relatively high temperature condition. Here, the authors develop a low-temperature crystallisation strategy to grow CsPbBr3 single crystals in water with improved charge transport properties and stability. [ABSTRACT FROM AUTHOR]

Detecting low dose rates of X-rays is critical for making safer radiology instruments, but is limited by the absorber materials available. Here, we develop bismuth oxyiodide (BiOI) single crystals into effective X-ray detectors. BiOI features complex lattice dynamics, owing to the ionic character of the lattice and weak van der Waals interactions between layers. Through use of ultrafast spectroscopy, first-principles computations and detailed optical and structural characterisation, we show that photoexcited charge-carriers in BiOI couple to intralayer breathing phonon modes, forming large polarons, thus enabling longer drift lengths for the photoexcited carriers than would be expected if self-trapping occurred. This, combined with the low and stable dark currents and high linear X-ray attenuation coefficients, leads to strong detector performance. High sensitivities reaching 1.1 × 103 μC Gyair−1 cm−2 are achieved, and the lowest dose rate directly measured by the detectors was 22 nGyair s−1. The photophysical principles discussed herein offer new design avenues for novel materials with heavy elements and low-dimensional electronic structures for (opto)electronic applications. The complex coupling between charge-carriers and phonons in bismuth oxyiodide (BiOI) are uncovered, showing how carrier localisation is avoided and long transport lengths achieved. As a result, BiOI is revealed to be highly effective for X-ray detection. [ABSTRACT FROM AUTHOR]

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm. [ABSTRACT FROM AUTHOR]

The problem of detecting dangerous or prohibited objects in luggage is a very important step during the implementation of Security setup at Airports, Banks, Government buildings, etc. At present, the most common techniques for detecting such dangerous objects are by using intelligent data analysis algorithms such as deep learning techniques on X-ray imaging or employing a human workforce for inferring the presence of these threat objects in the obtained X-ray images. One of the major challenges while using deep-learning methods to detect such objects is the lack of high-quality threat image data containing the "dangerous" objects (objects of interest) versus the non-threat image data in practical scenarios. So, to tackle this data scarcity problem, anomaly detection techniques using normal data samples have shown great promise. Also, among the available Deep Learning Strategies for anomaly detection for computer vision applications, generative adversarial networks have achieved state-of-the-art results. Considering these insights, we adopted a newly proposed architecture known as Skip-GANomaly and devised a modified version of it by using a UNet++ style generator which performed better than Skip-GANomaly, getting an AUC of 94.94% on Compass-XP, a public X-ray dataset. Finally, for targeting better latent space exploration, we combine these two architectures into an Ensemble, which gives another boost to the performance, getting an AUC of 96.8% on the same Compass-XP, a public X-ray dataset. To further validate the effectiveness of ensemble-based architecture, its performance was tested on patch-based training data on a subset of randomly chosen images of another huge public X-ray dataset named as SIXray, and obtained an AUC of 75.3% on this reduced dataset. To demonstrate the prowess of the discriminator and to bring some explainability to the working of our ensemble, we have used Uniform Manifold Approximation and Projection to plot the latent-space vectors for the dangerous and non-dangerous objects of the test-set; this analysis indicates that the Ensemble learns better features for separating the anomalous class from non-anomalous with respect to the individual architectures. Thus, our proposed architecture provides state-of-the-art results for threat object detection. Most importantly, our models are able to detect threat objects without ever being trained on images containing threat objects. [ABSTRACT FROM AUTHOR]

Scintillators, which are widely used as radiation detectors, are phosphors that release absorbed ionizing radiation energy as ultraviolet or visible light. Inorganic glass scintillators have several advantages over inorganic crystal scintillators, such as ease of fabrication and low costs. However, unlike inorganic crystals, which can emit up to tens of thousands of photons/MeV, inorganic glasses exhibit less than several hundred photons/MeV in most cases. Here, we studied an inorganic glass scintillator that exhibits a light yield of 2700 photons/MeV, which exceeds those of previous inorganic glass scintillators with high light yields of approximately 2000 photons/MeV. The density of this material is 3.28 g/cm3, which is relatively high among glass scintillators. Moreover, a fast scintillation decay with a decay time constant of 30.0 ns was obtained and is attributed to the 5d–4f transition of Ce3+. Thus, this glass is suitable for gamma- and X-ray detection, thereby expanding the practical applicability of inorganic glass scintillators. [ABSTRACT FROM AUTHOR]