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X-ray nanotomography is a powerful tool for the characterization of nanoscale materials and structures, but it is difficult to implement due to the competing requirements of X-ray flux and spot size. Due to this constraint, state-of-the-art nanotomography is predominantly performed at large synchrotron facilities. We present a laboratory-scale nanotomography instrument that achieves nanoscale spatial resolution while addressing the limitations of conventional tomography tools. The instrument combines the electron beam of a scanning electron microscope (SEM) with the precise, broadband X-ray detection of a superconducting transition-edge sensor (TES) microcalorimeter. The electron beam generates a highly focused X-ray spot on a metal target held micrometers away from the sample of interest, while the TES spectrometer isolates target photons with a high signal-to-noise ratio. This combination of a focused X-ray spot, energy-resolved X-ray detection, and unique system geometry enables nanoscale, element-specific X-ray imaging in a compact footprint. The proof of concept for this approach to X-ray nanotomography is demonstrated by imaging 160 nm features in three dimensions in six layers of a Cu-SiO2 integrated circuit, and a path toward finer resolution and enhanced imaging capabilities is discussed. [ABSTRACT FROM AUTHOR]

The detection of inelastically scattered soft x-rays with high energy resolution usually requires large grating spectrometers. Recently, photoelectron spectrometry for analysis of x-rays (PAX) has been rediscovered for modern spectroscopy experiments at synchrotron light sources. By converting scattered photons to electrons and using an electron energy analyser, the energy resolution for resonant inelastic x-ray scattering (RIXS) becomes decoupled from the x-ray spot size and instrument length. In this work, we develop PAX towards high energy resolution using a modern photoemission spectroscopy setup studying Ba2Cu3O4Cl2 at the Cu L 3-edge. We measure a momentum transfer range of 24% of the first Brillouin zone simultaneously. Our results hint at the observation of a magnon excitation below 100 meV energy transfer and show intensity variations related to the dispersion of dd -excitations. With dedicated setups, PAX can complement the best and largest RIXS instruments, while at the same time opening new opportunities to acquire RIXS at a range of momentum transfers simultaneously and combine it with angle-resolved photoemission spectroscopy in a single instrument. [ABSTRACT FROM AUTHOR]

The Snake is a remarkable Galactic Centre radio filament with a morphology characterized by two kinks along its ∼20 arcmin extent. The major and minor kinks are located where the filament is most distorted from a linear magnetized structure running perpendicular to the Galactic plane. We present Chandra , VLA, and MeerKAT data and report the detection of an X-ray and radio source at the location of the major kink. High-resolution radio images of the major kink reveal a compact source with a steep spectrum with spectral index α ∼ −2.7 surrounded by extended emission. The radio luminosity and steep spectrum of the compact source are consistent with a pulsar. We also show flattening of the spectrum and enhanced synchrotron emissivity away from the position of the major kink along the Snake, which suggests injection of relativistic particles along the Snake. We argue that the major kink is created by a fast-moving (∼500–1000 km s−1) object punching into the Snake, distorting its magnetic structure, and producing X-ray emission. X-ray emission pinpoints an active acceleration site where the interaction is taking place. A secondary kink is argued to be induced by the impact of the high-velocity object producing the major kink. [ABSTRACT FROM AUTHOR]

Purpose: We investigated the characteristics of radiation-induced current in nano-porous pellet and thin-film anodized tantalum exposed to kVp X-ray beams. We aim at developing a large area (≫cm2) thin-film radiation sensor for medical, national security and space applications. Methods: Large area (few cm2) micro-thin Ta foils were anodized and coated with a counter electrode made of conductive polymer. In addition, several types of commercial electrolytic porous tantalum capacitors were assembled and prepared for irradiation with kVp X-rays. We measured dark current (leakage) as well as transient radiation-induced currents as a function of external voltage bias. Results: Large transient currents (up to 50 nA) under X-ray irradiation (dose rate of about 3 cGy/s) were measured in Ta2O5 capacitors. Small nano-porous Ta and large-area flat Ta foil capacitors show similar current–voltage characteristic curve after accounting for different X-ray attenuation in capacitor geometry. The signal is larger for thicker capacitor oxide. A non-negligible signal for null external voltage bias is observed, which is explained by fast electron production in Ta foils. Conclusions: Anodized tantalum is a promising material for use in large-area, self-powered radiation sensors for X-ray detection and for energy harvesting. [ABSTRACT FROM AUTHOR]

In response to the problems of complex background, multi-scale dangerous goods and severe stacking in X-ray security images, this paper proposes a high-accuracy dangerous goods detection algorithm for X-ray security images based on the improvement of YOLOv7. Firstly, by combining the coordinate attention mechanism, the downsampling structure of the backbone network is improved to enhance the model's target feature localization ability. Secondly, a weighted bidirectional feature pyramid network is used as the feature fusion structure to achieve multi-scale feature weighted fusion and further simplify the network. Then, combined with dynamic snake convolution, a downsampling structure was designed to facilitate the extraction of features at different scales, providing richer feature representations. Finally, drawing inspiration from the idea of group convolution and combining it with Conv2Former, a feature extraction module called a multi-convolution transformer (MCT) was designed to enhance the network's feature extraction ability by combining multi-scale information. The improved YOLOv7 in this article was tested on the public datasets SIXRay, CLCXray, and PIDray. The average detection accuracy (mAP) of the improved model was 96.3%, 79.3%, and 84.7%, respectively, which was 4.7%, 2.7%, and 3.1% higher than YOLOv7. This proves the effectiveness and universality of the method proposed in this article. Compared to the current mainstream X-ray image dangerous goods detection models, this model effectively reduces the false detection rate of dangerous goods in X-ray security inspection images and has achieved significant improvement in the detection of small and multi-scale targets, achieving higher accuracy in dangerous goods detection. [ABSTRACT FROM AUTHOR]

This paper focuses on the intelligent detection of prohibited items in X‐ray images during the security checking process. An intelligent semantic segmentation model of prohibited items in X‐ray images is proposed based on the attention‐based object localization method. Based on the pre‐trained CNN classification framework, the attention mechanism can map the high‐layer semantic information of objects into the input space, while generating energy saliency maps to locate the prohibited items. In order to make the obtained attention maps discriminative, the lateral and contrastive inhibition strategies are introduced and combined together which can highlight the responses of activated neurons. Under the guidance of attention responses, two traditional image segmentation algorithms are employed to achieve the semantic segmentation results for the prohibited items detection in X‐ray images. The proposed semantic segmentation model relies on weakly supervised learning mechanism, and only depends on the category labels of prohibited items, which greatly avoids the work cost of data semantic annotation. The experimental results based on the public SIXray baseline and the self‐built X‐ray image database demonstrate the proposed method can achieve about 65% IoU localization precise averagely. In addition, comparison experiments were carried out with the state‐of‐the‐arts and ablation experiments to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Scintillators, which can convert high‐energy particles (X‐rays) into detectable low‐energy ultraviolet–visible–near‐infrared photons, are essential components of X‐ray detectors and show extensive practical applications in nondestructive detection and medical imaging. Traditionally, inorganic scintillators represented by CsI:Tl have achieved definite progress. However, the harsh preparation conditions, high production cost, and poor mechanical properties impede their potential development in the high‐end X‐ray imaging field. Organic–inorganic hybrid metal complexes could be excellent alternatives, by virtue of their structural and spectral tunability, good solution processability, and excellent photophysical properties. This review mainly focuses on eco‐friendly lead‐free metal (Mn2+, Cu+, Sb3+, Sn2+, Ge2+, Ln3+, etc.) complex scintillators. The luminescence mechanisms are introduced and the scintillation performance, such as light yield, limit of detection, imaging resolution, etc., is highlighted. Moreover, the current challenges and perspectives in this emerging field are described. It is hoped to provide some theoretical guidance for the continuous development of the new scintillator systems. [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]

Background: This study aimed to evaluate the impact of an AI-assisted fracture detection program on radiology residents' performance in pediatric and adult trauma patients and assess its implications for residency training. Methods: This study, conducted retrospectively, included 200 radiographs from participants aged 1 to 95 years (mean age: 40.7 ± 24.5 years), encompassing various body regions. Among these, 50% (100/200) displayed at least one fracture, totaling one hundred thirty-five fractures, assessed by four radiology residents with different experience levels. A machine learning algorithm was employed for fracture detection, and the ground truth was established by consensus among two experienced senior radiologists. Fracture detection accuracy, reporting time, and confidence were evaluated with and without AI support. Results: Radiology residents' sensitivity for fracture detection improved significantly with AI support (58% without AI vs. 77% with AI, p < 0.001), while specificity showed minor improvements (77% without AI vs. 79% with AI, p = 0.0653). AI stand-alone performance achieved a sensitivity of 93% with a specificity of 77%. AI support for fracture detection significantly reduced interpretation time for radiology residents by an average of approximately 2.6 s (p = 0.0156) and increased resident confidence in the findings (p = 0.0013). Conclusion: AI support significantly enhanced fracture detection sensitivity among radiology residents, particularly benefiting less experienced radiologists. It does not compromise specificity and reduces interpretation time, contributing to improved efficiency. This study underscores AI's potential in radiology, emphasizing its role in training and interpretation improvement. [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]

X-ray weld seam images carry vital information about welds. Leveraging graphic–text recognition technology enables intelligent data collection in complex industrial environments, promising significant improvements in work efficiency. This study focuses on using deep learning methods to enhance the accuracy and efficiency of detecting weld seam information. We began by actively gathering a dataset of X-ray weld seam images for model training and evaluation. The study comprises two main components: text detection and text recognition. For text detection, we employed a model based on the DBNet algorithm and tailored post-processing techniques to the unique features of weld seam images. Through model training, we achieved efficient detection of the text regions, with 91% precision, 92.4% recall, and a 91.7% F1 score on the test dataset. In the text recognition phase, we introduced modules like CA, CBAM, and HFA to capture the character position information and global text features effectively. This optimization led to a remarkable text line recognition accuracy of 93.4%. In conclusion, our study provides an efficient deep learning solution for text detection and recognition in X-ray weld seam images, offering robust support for weld seam information collection in industrial manufacturing. [ABSTRACT FROM AUTHOR]

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results. [ABSTRACT FROM AUTHOR]

In this paper, an overview of wide-bandgap (WBG) semiconductors for radiation detection applications is given. The recent advancements in the fabrication of high-quality wafers have enabled remarkable WBG semiconductor device applications. The most common 4H-SiC, GaN, and β-Ga2O3 devices used for radiation detection are described. The 4H-SiC and GaN devices have already achieved exceptional results in the detection of alpha particles and neutrons, thermal neutrons in particular. While β-Ga2O3 devices have not yet reached the same level of technological maturity (compared to 4H-SiC and GaN), their current achievements for X-ray detection indicate great potential and promising prospects for future applications. [ABSTRACT FROM AUTHOR]

The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogues of astrophysical sources. Classification is useful for the study of individual objects, statistics for population studies, as well as for anomaly detection, that is, the identification of new unexplored phenomena, including transients and spectrally extreme sources. Despite the importance of this task, classification remains challenging in X-ray astronomy due to the lack of optical counterparts and representative training sets. We develop an alternative methodology that employs an unsupervised machine learning approach to provide probabilistic classes to Chandra Source Catalog sources with a limited number of labelled sources, and without ancillary information from optical and infrared catalogues. We provide a catalogue of probabilistic classes for 8756 sources, comprising a total of 14 507 detections, and demonstrate the success of the method at identifying emission from young stellar objects, as well as distinguishing between small- and large-scale compact accretors with a significant level of confidence. We investigate the consistency between the distribution of features among classified objects and well-established astrophysical hypotheses such as the unified active galactic nucleus model. This provides interpretability to the probabilistic classifier. Code and tables are available publicly through GitHub. We provide a web playground for readers to explore our final classification at https://umlcaxs-playground.streamlit.app. [ABSTRACT FROM AUTHOR]

Due to the low effectiveness and poor quality of manual crimping of grounding wires, the author proposes the design of an automatic crimping device for connecting tube grounding wires based on intelligent fully automatic technology. The device consists of a microcontroller, an upper computer control interface, an electric push rod, an infrared sensor, a pressure transmitter, and other devices. The staff used the upper computer monitoring interface to set the relevant parameters for grounding wire crimping, and used X-ray digital imaging technology to measure the crimping size of the grounding wire. The size met the set parameter conditions. Through the PID control algorithm in the microcontroller, the stepper motor was controlled to push the clamp to move, completing the automatic crimping of the grounding wire. The X-ray detection method was introduced to detect the quality of the grounding wire after the crimping was completed. The experimental results show that the average deviation between the measured crimping size of the grounding wire and the actual measurement size by the automatic crimping device is only 0.06 mm, indicating that its measurement results are accurate; The success rate of crimping exceeds 95%. The above experimental results verify that the designed crimping device has high stability and reliability, and good quality detection effect. [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]

This article proposes a progressive frequency domain-guided depth model with adaptive preprocessing to solve the problem of defect detection with weak features based on X-ray images. In distinct intuitive surface defect detection tasks, non-destructive testing of castings using X-rays presents more complex and weak defect features, leading to lower accuracy and insufficient robustness on the part of current casting defect detection methods. To address these challenges, the proposed method establishes four specialized mechanisms to improve model accuracy. First, an adaptive image contrast enhancement method is proposed to enhance the features of defects in casting images to promote subsequent feature extraction and prediction. Second, a subtle clue mining module based on frequency domain attention is proposed to fully extract the discriminative features of casting defects. Third, a feature refinement module based on progressive learning is proposed to achieve a balance between feature resolution and semantic information. Finally, a refined deep regression supervision mechanism is designed to improve defect detection accuracy under strict intersection-to-union ratio standards. We established extensive ablation studies using casting defect images in GDXray, conducted detailed comparative experiments with other methods, and performed experiments to analyze the robustness of the resulting models. Compared with other X-ray defect detection methods, our framework achieves an average +4.6 AP. Compared to the baseline, our proposed refined deep regression supervision mechanism results in an improvement of 5.3 AP. [ABSTRACT FROM AUTHOR]

The latest synchrotron radiation sources have the capability to produce X-ray beams with a photon flux that can be up to three orders of magnitude higher than previous-generation facilities, and that are not manageable by the currently available 2D photon-counting pixel detectors. The construction of new detectors that exceed the limitations of existing devices is a critical strategic need. Developing such detectors is a challenge in terms of readout electronics as well as sensor material, particularly in the case of devices intended to operate at X-ray energies above 30 keV. The approach adopted at the ESRF to deal with this major difficulty is twofold: the use of a novel semiconductor material with improved electrical properties, high-flux CdZnTe, and the investigation of a specific readout scheme, incremental digital integration, via the XIDer project in collaboration with the University of Heidelberg. Incremental digital integration is a method intended to be less sensitive to variations of the dark current than the conventional charge integration readout. However, this readout scheme requires that the leakage current from the sensor material stays below a certain threshold to reduce the leakage contributions. This paper introduces the ESRF strategy and few examples of the methods employed to evaluate the performance and leakage current behavior of high-flux CdZnTe pixelated sensors. These examples illustrate the first results obtained with this material under moderate to very high X-ray irradiation fluxes of up to 1012 photons/mm²/s. [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]

In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model's integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model's high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment. [ABSTRACT FROM AUTHOR]

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network's learning rate to derive the optimal group's weight information and ultimately improve the model's detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time. [ABSTRACT FROM AUTHOR]

When first introduced, single-photon counting detectors reshaped crystallography at synchrotrons. Their fast readout speed enabled, for example, shutter-less data collection and fine slicing of the rotation angle and boosted the development of new experimental techniques like ptychography. Under optimal conditions, single-photon counting detectors provide an unlimited dynamic range with image noise only limited by the Poisson statistics of the incoming photons. Counting the pulses from individual photons, essentially what made the detectors so successful, also causes the main drawback, which is the loss of efficiency at high photon fluxes due to pulse pileup in the analog front end. To fully take advantage of diffraction-limited light sources, the next-generation single-photon counters need to improve their count rate capabilities in the same order of magnitude as the increased flux. Moreover, fast frame rates (a few kHz) are required to cope with the shorter dwell time achievable, thanks to the higher flux. Detector architecture with multiple comparators and counters can open new possibilities for energy-resolved imaging, while interpixel communication can overcome the issues arising from charge sharing and reduce the loss of efficiency at the pixel corners. Coupling single-photon counting detectors to high-Z sensors for hard X-ray detection (> 20 keV) and to low-gain avalanche diodes (LGADs) for soft X-rays is also necessary to make use of the increased coherence of the new light sources over the full radiation spectrum. In this paper, we present possible strategies to improve the performance of single-photon counting detectors at the fourth-generation synchrotron sources and compare them to charge integrating detectors. [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]

Precise control and characterization of nanomaterials at working conditions are essential for further rational applications in many areas important for modern society. Penetrating properties of X-ray radiation in combination with advanced spectroscopy schemes are an ideal tool to investigate modifications of nanomaterials with extraordinary precision. Here, we present preliminary results on the controlled oxidation of copper nanoparticles and exploration of X-ray absorption spectroscopy to follow electronic and structural changes. The described pilot experiment raises questions on the applicability of high energy resolution X-ray detection schemes in potential future investigations aimed at following reversible reduction/oxidation processes at nanoparticle surfaces. [ABSTRACT FROM AUTHOR]

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening lung injury for which early diagnosis and evidence-based treatment can improve patient outcomes. Chest X-rays (CXRs) play a crucial role in the identification of ARDS; however, their interpretation can be difficult due to non-specific radiological features, uncertainty in disease staging, and inter-rater variability among clinical experts, thus leading to prominent label noise issues. To address these challenges, this study proposes a novel approach that leverages label uncertainty from multiple annotators to enhance ARDS detection in CXR images. Label uncertainty information is encoded and supplied to the model as privileged information, a form of information exclusively available during the training stage and not during inference. By incorporating the Transfer and Marginalized (TRAM) network and effective knowledge transfer mechanisms, the detection model achieved a mean testing AUROC of 0.850, an AUPRC of 0.868, and an F1 score of 0.797. After removing equivocal testing cases, the model attained an AUROC of 0.973, an AUPRC of 0.971, and an F1 score of 0.921. As a new approach to addressing label noise in medical image analysis, the proposed model has shown superiority compared to the original TRAM, Confusion Estimation, and mean-aggregated label training. The overall findings highlight the effectiveness of the proposed methods in addressing label noise in CXRs for ARDS detection, with potential for use in other medical imaging domains that encounter similar challenges. [ABSTRACT FROM AUTHOR]

Identifying the classes and locations of prohibited items is the target of security inspection. However, X-ray security inspection images with insufficient feature extraction, imbalance between easy and hard samples, and occlusion lead to poor detection accuracy. To address the above problems, an object-detection method based on YOLOv8 is proposed. Firstly, an ASFF (adaptive spatial feature fusion) and a weighted feature concatenation algorithm are introduced to fully extract the scale features from input images. In this way, the model can learn further details in training. Secondly, CoordAtt (coordinate attention module), which belongs to the hybrid attention mechanism, is embedded to enhance the learning of features of interest. Then, the slide loss function is introduced to balance the simple samples and the difficult samples. Finally, Soft-NMS (non-maximum suppression) is introduced to resist the conditions containing occlusion. The experimental result shows that mAP (mean average precision) achieves 90.2%, 90.5%, 79.1%, and 91.4% on the Easy, Hard, and Hidden sets of the PIDray and SIXray public test set, respectively. Contrasted with original model, the mAP of our proposed YOLOv8n model increased by 2.7%, 3.1%, 9.3%, and 2.4%, respectively. Furthermore, the parameter count of the modified YOLOv8n model is roughly only 3 million. [ABSTRACT FROM AUTHOR]

Hybrid pixel detectors have become indispensable at synchrotron and X-ray free-electron laser facilities thanks to their large dynamic range, high frame rate, low noise, and large area. However, at energies below 3 keV, the detector performance is often limited because of the poor quantum efficiency of the sensor and the difficulty in achieving single-photon resolution due to the low signal-to-noise ratio. In this paper, we address the quantum efficiency of silicon sensors by refining the design of the entrance window, mainly by passivating the silicon surface and optimizing the dopant profile of the n+ region. We present the measurement of the quantum efficiency in the soft X-ray energy range for silicon sensors with several process variations in the fabrication of planar sensors with thin entrance windows. The quantum efficiency for 250 eV photons is increased from almost 0.5% for a standard sensor to up to 62% as a consequence of these developments, comparable to the quantum efficiency of backside-illuminated scientific CMOS sensors. Finally, we discuss the influence of the various process parameters on quantum efficiency and present a strategy for further improvement. [ABSTRACT FROM AUTHOR]

Hyperluminous infrared galaxies (HyLIRGs) are the most extreme star-forming systems observed in the early Universe, and their properties still elude comprehensive understanding. We have undertaken a large XMM – Newton observing programme to probe the total accreting black hole population in three HyLIRGs at z = 2.12, 3.25, and 3.55, gravitationally lensed by foreground galaxies. Selected from the Planck All-Sky Survey to Analyse Gravitationally lensed Extreme Starbursts (PASSAGES), these HyLIRGs have apparent infrared luminosities >1014 L⊙. Our observations revealed X-ray emission in each of them. PJ1336+49 appears to be dominated by high-mass X-ray binaries (HMXBs). Remarkably, the luminosity of this non-AGN X-ray emission exceeds by a factor of about 3 the value obtained by calibration with local galaxies with much lower star formation rates. This enhanced X-ray emission most likely highlights the efficacy of dynamical HMXB production within compact clusters, which is an important mode of star formation in HyLIRGs. The remaining two (PJ0116−24 and PJ1053+60) morphologically and spectrally exhibit a compact X-ray component in addition to the extended non-AGN X-ray emission, indicating the presence of Active Galactic Nuclei (AGNs). The AGN appears to be centrally located in the reconstructed source plane images of PJ0116−24, which manifests its star-forming activity predominantly within an extended galactic disc. In contrast, the AGN in the field of PJ1053+60 is projected 60 kpc away from the extreme star-forming galaxy and could be ejected from it. These results underline the synergistic potential of deep X-ray observations with strong lensing for the study of high-energy astrophysical phenomena in HyLIRGs. [ABSTRACT FROM AUTHOR]

We report on IXPE, NICER, and XMM–Newton observations of the magnetar 1E 2259+586. We find that the source is significantly polarized at about or above 20  per cent for all phases except for the secondary peak where it is more weakly polarized. The polarization degree is strongest during the primary minimum which is also the phase where an absorption feature has been identified previously. The polarization angle of the photons are consistent with a rotating vector model with a mode switch between the primary minimum and the rest of the rotation of the neutron star. We propose a scenario in which the emission at the source is weakly polarized (as in a condensed surface) and, as the radiation passes through a plasma arch, resonant cyclotron scattering off of protons produces the observed polarized radiation. This confirms the magnetar nature of the source with a surface field greater than about 1015 G. [ABSTRACT FROM AUTHOR]

The healthcare system in India faces an acute shortage of medical staff, significantly impacting the nation's ability to provide sufficient healthcare services. X-ray reports are paramount to orthopaedic surgeons for making critical patient care decisions. Implementing automated systems for detecting bone fractures offers a potentially efficient solution to supplement the limited workforce. However, these systems encounter substantial hurdles, such as inconsistent image quality and the risk of incorrect diagnosis due to the limitations of current technology. The research provides a specialised tool for identifying bone fractures with high precision (97% accuracy) and an excellent overall rating (F1 score of 0.98), showing it can reliably detect fractures. The assessment of this model is augmented by visual tools like heatmaps and performance graphs, providing an in-depth analysis of its operational effectiveness. Furthermore, creating a user-friendly interface is intended to streamline the adoption of this tool in clinical settings. Notwithstanding its demonstrated precision, the model must navigate data-related challenges, precisely data complexity and the representation of different categories, to prevent skewed outcomes. Clarifying the model's decision-making process and adaptability to various clinical situations are also critical. The study emphasises the need for thorough testing to ensure the model's dependability in diagnosing fractures before being deployed effectively in the medical field. [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]

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel machine-learning pipeline called Cavity Detection Tool (CADET), developed as an assistive tool that detects and estimates the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra -like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14 per cent at an 89 per cent true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5 per cent false-positive rate. When applied to real Chandra images, the pipeline recovered 93 out of 97 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered seven new cavity pairs in atmospheres of early-type galaxies (IC 4765, NGC 533, NGC 2300, NGC 3091, NGC 4073, NGC 4125, and NGC 5129) and a number of potential cavity candidates. [ABSTRACT FROM AUTHOR]

The recent serendipitous discovery of a new population of short duration X-ray transients, thought to be associated with collisions of compact objects or stellar explosions in distant galaxies, has motivated efforts to build up statistical samples by mining X-ray telescope archives. Most searches to date, however, do not fully exploit recent developments in the signal and imaging processing research domains to optimize searches for short X-ray flashes. This paper addresses this issue by presenting a new source detection pipeline, STATiX (Space and Time Algorithm for Transients in X-rays), which directly operates on three-dimensional X-ray data cubes consisting of two spatial and one temporal dimension. The algorithm leverages wavelet transforms and the principles of sparsity to denoise X-ray observations and then detect source candidates on the denoised data cubes. The light curves of the detected sources are then characterized using the Bayesian blocks algorithm to identify flaring periods. We describe the implementation of STATiX in the case of XMM–Newton data, present extensive validation and performance verification tests based on simulations and also apply the pipeline to a small subset of seven XMM–Newton observations, which are known to contain transients sources. In addition to known flares in the selected fields, we report a previously unknown short duration transient found by our algorithm that is likely associated with a flaring Galactic star. This discovery demonstrates the potential of applying STATiX to the full XMM–Newton archive. [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]

Crystalline organic–inorganic hybrids, which exhibit colorimetric responses to ionizing radiation, have recently been recognized as promising alternatives to conventional X‐ray dosimeters. However, X‐ray‐responsive organic–inorganic hybrids are scarce and the strategy to fine‐tune their detection sensitivity remains elusive. Herein, an unprecedented mixed‐ligand strategy is reported to modulate the X‐ray detection efficacy of organic–inorganic hybrids. Deliberately blending the stimuli‐responsive terpyridine carboxylate ligand (tpc−) and the auxiliary pba− group with different ratios gives rise to two OD thorium‐bearing clusters (Th‐102 and Th‐103) and a 1D coordination polymer (Th‐104). Notably, distinct X‐ray sensitivity is evident as a function of molar ratio of the tpc− ligand, following the trend of Th‐102 > Th‐103 > Th‐104. Moreover, Th‐102, which is exclusively built from the tpc− ligands with the highest degree of π–π interactions, exhibits the most sensitive radiochromic and fluorochromic responses toward X‐ray with the lowest detection limit of 1.5 mGy. The study anticipates that this mixed‐ligand strategy will be a versatile approach to tune the X‐ray sensing efficacy of organic–inorganic hybrids. [ABSTRACT FROM AUTHOR]

In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial for pneumonia detection. Rigorous evaluation demonstrates that our attention mechanism significantly enhances pneumonia detection accuracy, achieving a satisfactory result of 96% accuracy. To address the issue of imbalanced training samples, we integrate an enhanced focal loss into our architecture. This approach assigns higher weights to minority classes during training, effectively mitigating data imbalance. Our model's performance significantly improves, surpassing that of traditional approaches such as the pretrained ResNet-50 model. Our attention-enhanced architecture thus presents a powerful solution for pneumonia detection in chest X-ray images, achieving an accuracy of 98%. By integrating enhanced focal loss, our approach effectively addresses imbalanced training sample. Comparative analysis underscores the positive impact of our model's spatial and channel attention modules. Overall, our study advances pneumonia detection in medical imaging and underscores the potential of attention-enhanced architectures for improved diagnostic accuracy and patient outcomes. Our findings offer valuable insights into image diagnosis and pneumonia prevention, contributing to future research in medical imaging and machine learning. [ABSTRACT FROM AUTHOR]

Precisely acquiring the timing information of individual X-ray photons is important in both fundamental research and practical applications. The timing precision of commonly used X-ray single-photon detectors remains in the range of one hundred picoseconds to microseconds. In this work, we report on high-timing-precision detection of single X-ray photons through the fast transition to the normal state from the superconductive state of superconducting nanowires. We successfully demonstrate a free-running X-ray single-photon detector with a timing resolution of 20.1 ps made of 100-nm-thick niobium nitride film with an active area of 50  μ m by 50  μ m. By using a repeated differential timing measurement on two adjacent X-ray single-photon detectors, we demonstrate a precision of 0.87 ps in the arrival-time difference of X-ray photon measurements. Therefore, our work significantly enhances the timing precision in X-ray photon counting, opening a new niche for ultrafast X-ray photonics and many associated applications. [ABSTRACT FROM AUTHOR]

X-ray diffraction imaging (XDI) is utilized for visualizing the structures of noncrystalline particles in material sciences and biology. In the structural analysis, phase-retrieval (PR) algorithms are applied to the diffraction amplitude data alone to reconstruct the electron density map of a specimen particle projected along the direction of the incident X-rays. However, PR calculations may not lead to good convergence because of a lack of diffraction patterns in small-angle regions and Poisson noise in X-ray detection. Therefore, the PR calculation is still a bottleneck for the efficient application of XDI in the structural analyses of non-crystalline particles. For screening maps from hundreds of trial PR calculations, we have been using a score and measuring the similarity between a pair of retrieved maps. Empirically, probable maps approximating the particle structures gave a score smaller than a threshold value, but the reasons for the effectiveness of the score are still unclear. In this study, the score is characterized in terms of the phase differences between the structure factors of the retrieved maps, the usefulness of the score in screening the maps retrieved from experimental diffraction patterns is demonstrated, and the effective resolution of similarity-score-selected maps is discussed. [ABSTRACT FROM AUTHOR]

The paper presents the analysis of optically selected GAMA groups and clusters in the SRG/eROSITA X-ray map of eROSITA Final Equatorial Depth Survey, in the halo mass range 1013−5 × 1014 M⊙ and at z < 0.2. All X-ray detections have a clear GAMA counterpart, but most of the GAMA groups in the halo mass range 1013−1014 M⊙ remain undetected. We compare the X-ray surface brightness profiles of the eROSITA detected groups with the mean stacked profile of the undetected low-mass haloes at fixed halo mass. Overall, we find that the undetected groups exhibit less concentrated X-ray surface brightness, dark matter, and galaxy distributions with respect to the X-ray-detected haloes. The mean gas mass fraction profiles are consistent in the two samples within 1.5σ, indicating that the gas follows the dark matter profile. The low-mass concentration and the magnitude gap indicate that these systems are young. They reside with a higher probability in filaments while X-ray-detected groups favour the nodes of the Cosmic Web. Because of the lower central emission, the undetected systems tend to be X-ray underluminous at fixed halo mass and to lie below the LX − M halo relation. Interestingly, the X-ray-detected systems inhabiting the nodes scatter the less around the relation, while those in filaments tend to lie below it. We do not observe any strong relationship between the system X-ray appearance and the active galactic nucleus (AGN) activity. We cannot exclude the role of the past AGN feedback in affecting the gas distribution over the halo lifetime. However, the data suggests that the observed differences might be related to the halo assembly bias. [ABSTRACT FROM AUTHOR]

We report a comprehensive spectro-polarimetric study of the black hole binary LMC X-3 using simultaneous Imaging X-ray Polarimetry Explorer (IXPE), NICER , and NuSTAR observations in 0.5–20 keV energy band. The broad-band energy spectrum (0.5–20 keV) with NICER and NuSTAR is well described by the disc emission of temperature ∼1.1 keV and a weak Comptonizing tail beyond ∼10 keV. This evidently suggests a disc-dominated spectral state of the source with disc contribution of ∼96  per cent. The lack of variability (rms ∼ 0.5  per cent) in the power spectrum further corroborates the high/soft nature of the source. A significant polarization degree (PD) of 3.04 ± 0.40  per cent (>7σ) at a polarization angle of −44.24° ± 3.77° (>7σ) is found in 2–8 keV energy range of IXPE. In addition, PD is seen to increase with energy up to ∼4.35 ± 0.98  per cent (>3σ) in 4–8 keV band. Further, we attempt to constrain the source spin (a *) using broad-band spectral modelling that indicates a weakly rotating 'hole' in LMC X-3 with |$a_{*}=0.273_{-0.012}^{+0.011}\,\text{to}\,0.295_{-0.021}^{+0.008}$| (90  per cent confidence). Based on the spectro-polarimetric results, we infer that the polarization in LMC X-3 is resulted possibly due to the combined effects of the direct and/or reflected emissions from a partially ionized disc atmosphere. Finally, we discuss the relevance of our findings. [ABSTRACT FROM AUTHOR]

Signal amplification is vitally important for sensing low‐dose X‐rays in medical diagnosis by amplifying the generated electric read‐out signal. However, the complexity of external amplification circuits hampers device miniaturization and portability, while integrating amplification functionality directly into sensors or detectors remains a significant and formidable challenge. In this work, a direct high‐gain X‐ray detector with facile electron drift speed manipulation is reported in perovskite single‐crystal film (SCF). By employing laser‐assisted nucleation, high‐quality MAPbBr3 SCF is fabricated with precise control of thickness from ≈20 to ≈500 µm and the area up to 3 by 2 cm, while the architecture of ITO/MAPbBr3/Au is constructed to form the Schottky junction in opposite polarity. With the assistance of applied bias, the space electric field over MAPbBr3 SCF can be tunable, which ensures the manipulation of charge carrier drift speed to form recirculation for internal gain. The resultant photodetector exhibits an ultrahigh sensitivity of 1.44 × 105 µC Gy−1 cm−2 with a gain of 5.14 × 105, an ultralow detection limit of 39.8 nGy s−1, and the X‐ray array imaging is achieved at a low dose rate of 5 µGy s−1. These results confirm the advance of high‐gain detectors in constructing sensing arrays for practical safe medical diagnosis. [ABSTRACT FROM AUTHOR]

Scintillation semiconductors play increasingly important medical diagnosis and industrial inspection roles. Recently, two‐dimensional (2D) perovskites have been shown to be promising materials for medical X‐ray imaging, but they are mostly used in low‐energy (≤130 keV) regions. Direct detection of MeV X‐rays, which ensure thorough penetration of the thick shell walls of containers, trucks, and aircraft, is also highly desired in practical industrial applications. Unfortunately, scintillation semiconductors for high‐energy X‐ray detection are currently scarce. Here, This paper reports a 2D (C4H9NH3)2PbBr4 single crystal with outstanding sensitivity and stability toward X‐ray radiation that provides an ultra‐wide detectable X‐ray range of between 8.20 nGyair s−1 (50 keV) and 15.24 mGyair s−1 (9 MeV). The (C4H9NH3)2PbBr4 single‐crystal detector with a vertical structure is used for high‐performance X‐ray imaging, delivering a good spatial resolution of 4.3 lp mm−1 in a plane‐scan imaging system. Low ionic migration in the 2D perovskite enables the vertical device to be operated with hundreds of keV to MeV X‐ray radiation at high bias voltages, leading to a sensitivity of 46.90 μC Gyair−1 cm−2 (−1.16 V μm−1) with 9 MeV X‐ray radiation, demonstrating that 2D perovskites have enormous potential for high‐energy industrial applications. [ABSTRACT FROM AUTHOR]

High‐resolution X‐ray imaging is increasingly required for medical diagnosis and large‐area detection. However, the issues of scattering and optical crosstalk are limiting the spatial resolution of the indirect X‐ray imaging. In this study, a feasible and efficient strategy is proposed to in situ synthesize flexible Cs3Cu2I5:2%In+@paper as a superior scintillator film, which can be scaled up to an ultra‐large area of 4800 cm2. The as‐obtained Cs3Cu2I5:2%In+@paper performs a fascinating photoluminescence quantum efficiency up to 88.14%, a steady state light yield of 70169 photons/MeV, and spatial resolution of 15 lp mm−1. Moreover, the suppressed physical scattering and optical crosstalk of the corresponding film are demonstrated. Accordingly, this work explores a feasible fabrication of customizable scintillation films with large area for high‐resolution X‐ray detection. [ABSTRACT FROM AUTHOR]

X-ray detection for personal dosimetry requires sensitive, stable and non-toxic materials. At the same time, scalability onto large-area and flexible substrates is emerging as a desirable property. To satisfy these requirements, novel materials to be employed as the active layer of direct X-ray detectors are needed. In this search for easy-processability, large area, efficient and non-toxic materials for direct X-ray detection, we assess the performance of a layered metal-organic chalcogenide [AgSePh]M, recently proposed as representative of a novel excitonic semiconductors platform. Here we demonstrate that [AgSePh]M can be successfully applied as direct ionizing radiation detecting layer, reaching sensitivities up to (180 ± 10) ^C Gy-1 cm-2 and competitive limit of detection down to (100 ± 30) nGy s-1. Moreover, it offers good stability and reproducibility of detection after 100 Gy of irradiation and upon bending to a curvature radius of 5 mm. [ABSTRACT FROM AUTHOR]

In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is capable of accurate object detection, which provides a bounding box denoting the area in the X-rays that shows some possibility of TB (tuberculosis). The system makes use of CNNs (Convolutional Neural Networks) and YOLO models for the detection of the consolidation of cavitary patterns of the lesions and their detection, respectively. For this study, we experimented on the TBX11K dataset, which is a publicly available dataset. In our experiment, we employed class weights and data augmentation techniques to address the data imbalance present in the dataset. This technique shows a promising improvement in the model's performance and thus better generalization. In addition, it also shows that the developed model achieved promising results with a mAP (mean average precision) of 0.587, addressing class imbalance and yielding a robust performance for both obsolete pulmonary TB and active TB detection. Thus, our CAD system, rooted in state-of-the-art deep-learning and computer vision methodologies, not only advances diagnostic accuracy but also contributes to the mitigation of TB transmission risks. The substantial improvement in the model's performance and the ability to handle class imbalance underscore the potential of our approach for real-world TB 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]

X-ray images are an important industrial non-destructive testing method. However, the contrast of some weld seam images is low, and the shapes and sizes of defects vary greatly, which makes it very difficult to detect defects in weld seams. In this paper, we propose a gray value curve enhancement (GCE) module and a model specifically designed for weld defect detection, namely WD-YOLO. The GCE module can improve image contrast to make detection easier. WD-YOLO adopts feature pyramid and path aggregation designs. In particular, we propose the NeXt backbone for extraction and fusion of image features. In the YOLO head, we added a dual attention mechanism to enable the model to better distinguish between foreground and background areas. Experimental results show that our model achieves a satisfactory balance between performance and accuracy. Our model achieved 92.6% mAP@0.5 with 98 frames per second. [ABSTRACT FROM AUTHOR]

In this study, it is shown how the photoluminescence, scintillation, and optical thermometric properties are managed via the introduction of Gd3+ ions into Pr3+:Lu2Zr2O7. Raman spectra validate that partial replacement of Lu3+ with Gd3+ can promote the phase transition of Lu2Zr2O7 host from the defective fluorite structure to the ordered pyrochlore one. Upon 289 nm excitation, all the samples emit the 483 (3P0 → 3H4), 581 (1D2 → 3H4), 611 (3P0 → 3H6), 636 (3P0 → 3F2), and 714 nm (3P0 → 3F4) emissions from Pr3+ ions, which are enhanced with the addition of Gd3+ ions due to the modification of crystal structure. Dissimilarly, the X‐ray excited luminescence spectra consist of a strong emission located at 314 nm from Gd3+ ions (6P7/2 → 8S7/2), together with the typical emissions from Pr3+ ions, which exhibit different dependences on the Gd3+ concentration. When the luminescence intensity ratio between the 3P0 → 3H6 (611 nm) and 1D2 → 3H4 (581 nm) transitions is selected for temperature sensing, Pr3+:(Lu0.75Gd0.25)2Zr2O7 shows the optimal relative sensing sensitivity of 0.78% K−1 at 303 K, which is higher than that of the Gd3+‐free sample. Therefore, the developed Pr3+:(Lu, Gd)2Zr2O7 phosphors have the applicative potential for optical thermometry, X‐ray detection, and photodynamic therapy. [ABSTRACT FROM AUTHOR]