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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

The coronavirus occurred in Wuhan (China) first and it was declared a global pandemic. To detect coronavirus X-ray images can be used. Convolutional neural networks (CNNs) are used commonly to detect illness from images. There can be lots of different alternative deep CNN models or architectures. To find the best architecture, hyper-parameter optimization can be used. In this study, the problem is modeled as a multi-objective optimization (MOO) problem. Objective functions are multi-class cross entropy, error ratio, and complexity of the CNN network. For the best solutions to the objective functions, multi-objective hyper-parameter optimization is made by NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, MOEA/D, and proposed Swarm Genetic Algorithms (SGA). SGA is a swarm-based algorithm with a cross-over process. All six algorithms are run and give Pareto optimal solution sets. When the figures obtained from the algorithms are analyzed and algorithm hypervolume values are compared, SGA outperforms the NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, and MOEA/D algorithms. It can be concluded that SGA is better than others for multi-objective hyper-parameter optimization algorithms for COVID-19 detection from X-ray images. Also, a sensitivity analysis has been made to understand the effect of the number of the parameters of CNN on model success. [ABSTRACT FROM AUTHOR]