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Machine learning methods have been extensively employed to predict COVID-19 using chest X-ray images in numerous studies. However, a machine learning model must exhibit robustness and provide reliable predictions for diverse populations, beyond those used in its training data, to be truly valuable. Unfortunately, the assessment of model generalisability is frequently overlooked in current literature. In this study, we investigate the generalisability of three classification models – ResNet50v2, MobileNetv2, and Swin Transformer – for predicting COVID-19 using chest X-ray images. We adopt three concurrent approaches for evaluation: the internal-and-external validation procedure, lung region cropping, and image enhancement. The results show that the combined approaches allow deep models to achieve similar internal and external generalisation capability. [ABSTRACT FROM AUTHOR]

Canine Hip Dysplasia (CHD) is a congenital disease with a polygenic hereditary component, characterised by abnormal development of the coxo-femoral joint which results in poor coaptation of the femoral head in the acetabulum; the disease rapidly progresses to osteoarthritis of the hip. While dysplasia has been recognised in practically all canine breeds, it is much more common and of concern in medium and large dog breeds with rapid development. Dysplasia in predisposed breeds, particularly the German Shepherd, is the object of screening based on systematic radiological control in some countries. Our collected dataset comprises 507 X-ray images of dogs affected by hip dysplasia (HD). These images were meticulously evaluated using six Deep Convolutional Neural Network (CNN) models. Following an extensive analysis of the top-performing models, VGG16 emerged as the leader, achieving remarkable accuracy, recall, and precision scores of 98.32%, 98.35%, and 98.44%, respectively. Leveraging deep learning (DL) techniques, this approach excels in diagnosing CHD from hip X-rays with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

In the evaluation of cervical spine disorders, precise positioning of anatomo-physiological hallmarks is fundamental for calculating diverse measurement metrics. Despite the fact that deep learning has achieved impressive results in the field of keypoint localization, there are still many limitations when facing medical image. First, these methods often encounter limitations when faced with the inherent variability in cervical spine datasets, arising from imaging factors. Second, predicting keypoints for only 4% of the entire X-ray image surface area poses a significant challenge. To tackle these issues, we propose a deep neural network architecture, NF-DEKR, specifically tailored for predicting keypoints in cervical spine physiological anatomy. Leveraging neural memory ordinary differential equation with its distinctive memory learning separation and convergence to a singular global attractor characteristic, our design effectively mitigates inherent data variability. Simultaneously, we introduce a Multi-Resolution Focus module to preprocess feature maps before entering the disentangled regression branch and the heatmap branch. Employing a differentiated strategy for feature maps of varying scales, this approach yields more accurate predictions of densely localized keypoints. We construct a medical dataset, SCUSpineXray, comprising X-ray images annotated by orthopedic specialists and conduct similar experiments on the publicly available UWSpineCT dataset. Experimental results demonstrate that compared to the baseline DEKR network, our proposed method enhances average precision by 2% to 3%, accompanied by a marginal increase in model parameters and the floating-point operations (FLOPs). The code (https://github.com/Zhxyi/NF-DEKR) is available. [ABSTRACT FROM AUTHOR]

Double shell capsule can provide a potential low-convergence to fusion ignition at relatively low temperature (∼3 keV). One of the main sources of degrading double shell implosion performance is the low-mode asymmetries. Recently, the experiments on the evolution of low-mode asymmetries introduced by x-ray P2 drive asymmetry during double shell implosions were carried out on the SG facility, where the outer shell and inner shell shapes were measured through the backlit radiography, and the fuel shape near stagnation was measured by core x-ray self-emission imaging. The time-dependent x-ray flux symmetry was controlled by varying the inner cone fraction, defined as the ratio of the inner cone power to the total laser power, while keeping the drive temperature histories same across experiments. Both the hohlraum radiation and the capsule implosions were analyzed using a two-dimensional radiation-hydrodynamics code. Comparing the experimental radiographs and self-emission images to the simulations, it is found that the simulated outer shell, inner shell and hot spot shapes are in qualitative agreement with experiments, especially, the symmetry swings of the hot spot shape near stagnation are observed from both experimental and simulation results. Further, the effect of x-ray drive asymmetries on double shell implosion performance is preliminarily investigated using numerical simulations. We find that the azimuthal variations in radial velocity caused by drive asymmetries can generate azimuthal mass flow of the inner shell, thus kinetic energy of the inner shell would be not converted into fuel internal energy with high efficiency, and the mass-averaged ion temperature of the fuel at stagnation would be reduced. [ABSTRACT FROM AUTHOR]

Early in 2019, COVID-19 was discovered for the first time in Wuhan, China, resulting in the deaths of a significant number of people in many different countries all over the world. Due to the rapid spread of this epidemic, scientists have strived to find quick and accurate diagnostic methods to lessen its global impact. Chest X-ray images were the best tool for rapidly and safely detecting COVID-19, but the manual examination of those images might result in faulty diagnoses. Therefore, the scientists have used deep learning (DL) models to remedy this shortcoming and classify the images infected with COVID-19 more accurately. Image segmentation is an essential step in improving the classification accuracy of DL models. Among existing image segmentation techniques, multilevel thresholding-based image segmentation techniques have gained significant interest due to their simplicity and high accuracy. However, the computational cost of those techniques exponentially increases as the number of threshold levels increases. Therefore, over the last few years, metaheuristic algorithms have collaborated with those techniques to significantly lessen the computational cost and accurately solve the image segmentation problem. However, those algorithms have some shortcomings, such as falling into local minima and slow convergence speed, which make them unable to find precise results. Therefore, in this paper, we present a new multilevel thresholding-based medical image segmentation technique based on the recently proposed spider wasp optimizer (SWO) to better segment the medical images, especially the chest X-ray images for detecting COVID-19 infection more accurately and rapidly. In addition, SWO is enhanced by two newly proposed mechanisms, namely global search improvement and local search improvement, to present a new better variant, namely improved SWO (ISWO). The former mechanism is responsible for improving the exploration operator by sharing the knowledge of the current individual and a newly generated individual, while the latter aims to improve the exploitation operator to improve the convergence speed. To evaluate the stability of ISWO and SWO, ten COVID-19 X-ray images with heterogeneous histograms under nine threshold levels (T) are used. Also, they are compared to eight rival optimizers according to several performance metrics to demonstrate their efficacy. According to the experimental results, ISWO is the best-performing algorithm, followed by SWO. Quantitatively, ISWO could achieve an average fitness value of 2796.837, while SWO could reach a value of 2796.33. [ABSTRACT FROM AUTHOR]

Phase transition at the initial solidification stage notably affects subsequent phase structure of peritectic alloys, however, detailed dynamics between entangled three-phase region remains ambiguous in the far-from-equilibrium condition during laser additive manufacturing. Here, in-situ peritectic phase transition during single-layer laser printing of an exemplary 304L stainless steel is directly investigated and quantified at high temporospatial resolution by high-speed synchrotron X-ray diffraction and imaging. These quantitative observations clarify the mechanism of coupled peritectic growth and local remelting at three-phase region. The results revealed provide in-depth understanding of phase transformation dynamics and delicate design of phase structure for metal additive manufacturing. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the effect of implant body diameter, platform diameter, and the use of transepithelial components on implant-abutment connection (IAC) microgap width. Materials and Methods: In total, 16 tests were performed on four commercial dental restoration models (BTI Biotechnology Institute). Different static loads were applied to the embedded implants according to the International Organization for Standardization (ISO) 14801, using a customized loading device. Measurements of the microgap were taken by means of highly magnified x-ray projection in situ in a micro-CT scanner. Regression models were obtained and compared through an analysis of covariance (ANCOVA). To quantify the effect of each variable, t tests (α = .05) of experimental results were performed. Results: Under 400 N, using a transepithelial component for the dental restoration, the microgap width was reduced by 20% (P = .044). Meanwhile, a 22% microgap reduction was observed when the implant body diameter was increased by 1 mm (P = .024). Finally, increasing the platform diameter by 1.4 mm led to a microgap reduction of 54% (P = .001). Conclusion: The use of a transepithelial component in dental restorations reduces the microgap width in IACs. Furthermore, given sufficient space for the implantation, larger implant bodies and platform diameters can also be used for this purpose. [ABSTRACT FROM AUTHOR]

Abstract X‐ray security checks aim to detect contraband in luggage; however, the detection accuracy is hindered by the overlapping and significant size differences of objects in X‐ray images. To address these challenges, the authors introduce a novel network model named Multi‐Scale Feature Attention (MSFA)‐DEtection TRansformer (DETR). Firstly, the pyramid feature extraction structure is embedded into the self‐attention module, referred to as the MSFA. Leveraging the MSFA module, MSFA‐DETR extracts multi‐scale feature information and amalgamates them into high‐level semantic features. Subsequently, these features are synergised through attention mechanisms to capture correlations between global information and multi‐scale features. MSFA significantly bolsters the model's robustness across different sizes, thereby enhancing detection accuracy. Simultaneously, A new initialisation method for object queries is proposed. The authors’ foreground sequence extraction (FSE) module extracts key feature sequences from feature maps, serving as prior knowledge for object queries. FSE expedites the convergence of the DETR model and elevates detection accuracy. Extensive experimentation validates that this proposed model surpasses state‐of‐the‐art methods on the CLCXray and PIDray datasets.

Signal-to-noise ratio and spatial resolution are quantitatively analysed in the context of in-line (propagation based) X-ray phase-contrast imaging. It is known that free-space propagation of a coherent X-ray beam from the imaged object to the detector plane, followed by phase retrieval in accordance with Paganin's method, can increase the signal-to-noise in the resultant images without deteriorating the spatial resolution. This results in violation of the noise-resolution uncertainty principle and demonstrates `unreasonable' effectiveness of the method. On the other hand, when the process of free-space propagation is performed in software, using the detected intensity distribution in the object plane, it cannot reproduce the same effectiveness, due to the amplification of photon shot noise. Here, it is shown that the performance of Paganin's method is determined by just two dimensionless parameters: the Fresnel number and the ratio of the real decrement to the imaginary part of the refractive index of the imaged object. The relevant theoretical analysis is performed first, followed by computer simulations and then by a brief test using experimental images collected at a synchrotron beamline. More extensive experimental tests will be presented in the second part of this paper.

Sampling liquids in small and confined spaces to retrieve chemicals and microbiomes could enable minimally invasive monitoring human physiological conditions for understanding disease development and allowing early screening. However, existing tools are either invasive or too large for sampling liquids in tortuous and narrow spaces. Here we report a fundamental liquid sampling mechanism that enables millimeter-scale soft capsules for sampling liquids in confined spaces. The miniature capsule is enabled by flexible magnetic valves and superabsorbent polymer, fully wirelessly controlled for on-demand fluid sampling. A group of miniature capsules could navigate in fluid-filled and confined spaces safely using a rolling locomotion. The integration of on-demand triggering, sampling, and sealing mechanism and the agile group locomotion allows us to demonstrate precise control of the soft capsules, navigating and sampling body fluids in a phantom and animal organ ex vivo, guided by ultrasound and x-ray medical imaging. The proposed mechanism and wirelessly controlled devices spur the next-generation technologies for minimally invasive disease diagnosis. [ABSTRACT FROM AUTHOR]

Background and aim: Calibrated pelvic X-ray images are needed in the preoperative planning of total hip arthroplasty (THA) to predict component sizes. Errors and mismatch in the size of one or more components are reported, which can lead to clinically relevant complications. Our aim is to investigate whether we can solve the fundamental problem of X-ray calibration and whether traditional X-ray still has a place in preoperative planning despite improved radiological alternatives. Methods: Based on geometric and radiographic principles, we estimate that the magnification factor is adapted to the X-ray device and depends strongly on the source-image distance of the device. We analyse the errors of the various calibration methods and investigate which narrow range can be expected to show that the center of rotation is sufficiently accurate. Based on the results of several CT-scans we defined an adapted magnification factor and validated the degree of measurement accuracy. Results: The true magnification of objects on X-ray images depends mainly on the device settings. Stem size prediction is possible to a limited extent, with an error margin of 4.3%. Components can be predicted with a safety margin of one size up and down as with CT or 3D images. The prerequisite is that the source-image distance is greater than or equal to 120 cm, the table-image distance is known, and the object-image distance is estimated according to the patient's BMI. We defined a device-adapted magnification factor that simplifies the templating routine and can be used to obtain the most reliable preoperative dimensional measurements that can be expected from X-ray images. We found the error margin of the magnification factor with the highest degrees of prediction and precision. Conclusion: Preoperative planning is reliable and reproducible using X-ray images if calibration is performed with the device-adapted magnification factor suggested in this paper. [ABSTRACT FROM AUTHOR]

Background: Patients in the intensive care unit (ICU) are highly susceptible to malnutrition, and while enteral nutrition via nasogastric tube is the preferred method, there is a risk of inadvertent reflux and aspiration. Therefore, clinicians have turned to nasointestinal tubes (NET) for enteral nutrition as an alternative option. But the precise localization of NET presents an ongoing challenge. We proposed an innovative approach to provide a valuable reference for clinicians involved in NET placement. Method: Data were obtained retrospectively from the medical records of adult patients with a high risk of aspiration or gastric feeding intolerance who had a NET placed in the ICU of Zhejiang Provincial People's Hospital between October 1, 2017, and October 1, 2023. The collected data were subjected to statistical analysis using SPSS and R software. Result: There were 494 patients who met the inclusion and exclusion criteria. The first-pass success rate was 81.4% (n = 402). The success of a patient's initial NET placement was found to be associated with Angle SPC and Distance CP, as determined by univariate analysis (25.6 ± 16.7° vs. 41.9 ± 18.0°, P < 0.001; 40.0 ± 26.2 mm vs. 62.0 ± 31.8 mm, P < 0.001, respectively). By conducting a multivariate regression analysis, we identified a significant association between pyloric types and the success rate of placing NET (OR 29.559, 95%CI 14.084–62.038, P < 0.001). Conclusion: Angle SPC, Distance CP, and the type of pylorus are independently associated with successful initial placement of NET. Besides, patients with the outside type of pylorus (OP-type) exhibit a higher rate of initial placement success. [ABSTRACT FROM AUTHOR]

3D metal halide perovskite X‐ray detectors have become the focus of research in recent years due to their superior sensitivity. However, the reported efficient perovskite X‐ray detectors all contain highly toxic lead and worrisome stability, which greatly limits their wide application and promotion. In this study, a distorted 3D germanium perovskite MAGeI3 single crystal (SC) is designed and grown by gradient cooling method. Due to its distorted octahedral structure, the MAGeI3 SCs not only possess high sensitivity but also have superior long‐term working stability. Specifically, the MAGeI3 SC X‐ray detectors achieve a record sensitivity of 42825 µC Gy−1 cm−2 and an impressive low detection limit of 4.1 nGy s−1, both of them are far better than the state‐of‐the‐art lead‐free perovskite SC X‐ray detectors. Moreover, the unencapsulated SC detector still maintains the original response even at high applied electric field and high dose rate X‐ray irradiation for several hours. Eventually, a high resolution of 7.2 lp mm−1 in X‐ray imaging is demonstrated on the MAGeI3 SC detector under a low dose rate of 478 nGy s−1 and at a low electric field of 1 V mm−1. This work provides a new pathway to design high‐performance and low‐toxicity X‐ray detectors. [ABSTRACT FROM AUTHOR]

Advances in attosecond spectroscopy have enabled tracing and controlling the electron motion dynamics in matter, although they have yielded insufficient information about the electron dynamic in the space domain. Hence, ultrafast electron and x-ray imaging tools have been developed to image the ultrafast dynamics of matter in real time and space. The cutting-edge temporal resolution of these imaging tools is on the order of a few tens to a hundred femtoseconds, limiting imaging to the atomic dynamics and leaving electron motion imaging out of reach. Here, we obtained the attosecond temporal resolution in the transmission electron microscope, which we coined "attomicroscopy." We demonstrated this resolution by the attosecond diffraction measurements of the field-driven electron dynamics in graphene. This attosecond imaging tool would provide more insights into electron motion and directly connect it to the structural dynamics of matter in real-time and space domains, opening the door for long-anticipated real-life attosecond science applications in quantum physics, chemistry, and biology. [ABSTRACT FROM AUTHOR]

Introduction: The cardiothoracic ratio (CTR) based on postero-anterior chest X-rays (P-A CXR) images is one of the most commonly used cardiac measurement methods and an indicator for initially evaluating cardiac diseases. However, the hearts are not readily observable on P-A CXR images compared to the lung fields. Therefore, radiologists often manually determine the CTR's right and left heart border points of the adjacent left and right lung fields to the heart based on P-A CXR images. Meanwhile, manual CTR measurement based on the P-A CXR image requires experienced radiologists and is timeconsuming and laborious. Methods: Based on the above, this article proposes a novel, fully automatic CTR calculation method based on lung fields abstracted from the P-A CXR images using convolutional neural networks (CNNs), overcoming the limitations to heart segmentation and avoiding errors in heart segmentation. First, the lung field mask images are abstracted from the P-A CXR images based on the pre-trained CNNs. Second, a novel localization method of the heart's right and left border points is proposed based on the two-dimensional projection morphology of the lung field mask images using graphics. Results: The results show that the mean distance errors at the x-axis direction of the CTR's four key points in the test sets T1 (21 × 512 × 512 static P-A CXR images) and T2 (13 × 512 × 512 dynamic P-A CXR images) based on various pre-trained CNNs are 4.1161 and 3.2116 pixels, respectively. In addition, the mean CTR errors on the test sets T1 and T2 based on four proposed models are 0.0208 and 0.0180, respectively. Discussion: Our proposed model achieves the equivalent performance of CTR calculation as the previous CardioNet model, overcomes heart segmentation, and takes less time. Therefore, our proposed method is practical and feasible and may become an effective tool for initially evaluating cardiac diseases. [ABSTRACT FROM AUTHOR]

Tuberculosis caused by the infection of Mycobacterium. It is the fifth major source of death and one of the greatest threats to humans in the modern world. Thus, it needs to be detected at an earlier stage using the chest X-rays (CXR) image for precise identification and treatment. The suggested scheme's main goal is to identify this deadly disease using CXR with improved classification accuracy. This detection process comprises pre-processing, noise removal, balancing of image level, application of the Double Attention Res-U-Net-based Deep Neural Network (DARUNDNN) model, and optimization of deep learning features using the Dingo Optimization Algorithm for achieving better accuracy. The experimental validation of the proposed DARUNDNN model is conducted using benchmark datasets, namely Montgomery, Shenzhen, and National Institutes of Health CXR images. The results obtained using the Shenzhen dataset confirm that the proposed DARUNDNN model is efficient in achieving better accuracy of 98.92%, specificity of 97.24%, and sensitivity of 98.86% with a least error of 1.6 compared to the benchmarked models used for investigation. Moreover, the experimental validation conducted using the Montgomery County dataset also confirmed an excellent accuracy of 98.982%, a specificity of 97.56%, and a sensitivity of 98.52%, with a least error of 1.32 compared to the baseline approaches used for investigation. [ABSTRACT FROM AUTHOR]

Pneumonia is an acute respiratory illness caused by viruses or bacteria. Early detection of pneumonia is important to ensure curative treatment and improve survival rates. Pneumonia detection on chest X-rays (CXR) is important for early diagnosis, effective treatment, monitoring patient progress, and managing public health concerns. It plays a vital role in ensuring that individuals with pneumonia receive the appropriate care they need while contributing to research and disease surveillance efforts. However, the examination of CXRs is a difficult process and is prone to subjective variabilities. The use of artificial intelligence (AI) and deep learning (DL) models can perform the detection and classification of pneumonia on CXR images. With this motivation, this study introduces a new Chaotic Sea Horse Optimization with Deep Learning Method for Pneumonia Detection and Classification (CSHODL-PDC) technique on CXR images. The main intention of the CSHODL-PDC algorithm lies in the automated detection and classification of pneumonia on CXR images. The CSHODL-PDC method initially designs a Gaussian filtering (GF) based noise eradication approach to eliminate the noise. In addition, the CSHODL-PDC technique employs the NASNetLarge model to produce a set of feature vectors. Moreover, an improved fuzzy deep neural network (FDNN) model is applied for the automated identification and classification of pneumonia. Finally, the CSHO algorithm selects the optimal hyperparameter values of the improved FDNN model, demonstrating the novelty of the work. A series of simulation analyses were performed on the CXR Pneumonia dataset from the Kaggle repository. The experimental values inferred the improved performance of the CSHODL-PDC method over recent models with a maximum accuracy of 99.22%, precision of 98.96%, and recall of 99.22%. Therefore, the proposed model can be employed for accurate and automated pneumonia detection. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to evaluate the radiation knowledge and awareness levels of healthcare workers working in a single center in Mogadishu, Somalia, in terms of age, gender and education levels of the participants.Materials and methods: The research involved 274 healthcare professionals, comprising volunteer physician and other healthcare personnel working at the Somali-Turkey Recep Tayyip Erdoğan Training and Research Hospital in Mogadishu. The face-to-face questionnaire consists of 13 questions covering radiation-sensitive or resistant tissues, radiation status of medical imaging devices, biological effects of radiation, ionizing and non-ionizing radiation symbols and radiation protection principles. The study was limited to the health personnel in the hospital. The survey had 274 participants, comprising 126 (46%) women and 148 (54%) men. Of these, 82 (29.9%) were physicians and 192 (70.1%) were other healthcare professionals.Results: It was found that health workers under the age of thirty demonstrated a significantly higher level of knowledge about radiation protection principles than their counterparts over the age of thirty (p = 0.003). Similarly, it was observed that the level of awareness regarding the extent of knowledge about radiation protection was higher in health workers under thirty years of age than in those over thirty years of age (p: 0.001). It was found to be statistically significant that males were more aware of the potential harms of radiation than females (p: 0.004). It was found to be statistically significant that men were more aware of the radiation content of mammography and X-ray imaging methods than women (p: 0.002 and p: 0.029, respectively).Conclusions: To enhance the number of healthcare professionals in Mogadishu, Somalia, who are aware of the risks associated with radiation and the methods for its mitigation, it was postulated that a comprehensive radiation information and awareness training program could be beneficial. [ABSTRACT FROM AUTHOR]

The dynamics of Earth's magnetopause, driven by several different external/internal physical processes, plays a major role in the geospace energy budget. Given magnetopause motion couples across many space plasma regions, numerous forms of observations may provide valuable information in understanding these dynamics and their impacts. In-situ multi-point spacecraft measurements measure the local plasma environment, dynamics and processes; with upcoming swarms providing the possibility of improved spatiotemporal reconstruction of dynamical phenomena, and multi-mission conjunctions advancing understanding of the "mesoscale" coupling across the geospace "system of systems." Soft X-ray imaging of the magnetopause should enable boundary motion to be directly remote sensed for the first time. Indirect remote sensing capabilities might be enabled through the field-aligned currents associated with disturbances to the magnetopause; by harnessing data from satellite mega-constellations in low-Earth orbit, and taking advantage of upgraded auroral imaging and ionospheric radar technology. Finally, increased numbers of closely-spaced ground magnetometers in both hemispheres may help discriminate between high-latitude processes in what has previously been a "zone of confusion." Bringing together these multiple modes of observations for studying magnetopause dynamics is crucial. These may also be aided by advanced data processing techniques, such as physics-based inversions and machine learning methods, along with comparisons to increasingly sophisticated geospace assimilative models and simulations. [ABSTRACT FROM AUTHOR]

The geometry of an orifice is a major determinant of nozzle internal flow and cavitation, which directly govern spray atomization and consequently affect combustion and fuel economy in internal combustion engines. In this study, simulation models of the nozzle at different angles between the normal and the injection hole inlet cross-section and the injection hole axis, as well as with different injection hole cone angles (a positive angle between the injection hole axis and its walls implies a divergent hole), were investigated by means of a previously developed numerical model that was validated based on experimental data from X-ray image technology. The results indicate that as the angle between the normal and the injection hole inlet cross-section and the injection hole axis increases, the cavitation asymmetry within the injection hole intensifies, accompanied by a decrease in the gas volume fraction. On one side of the injection hole, the hydraulic flip width expands, while, on the other side of the injection hole, the flow state gradually changes from hydraulic flip to super-cavitation flow, transitional cavitation and fully reattached flow. The divergent orifice layout intensifies cavitation, and the higher the positive injection hole cone angle, the bigger the hydraulic flip width. The convergent layout of the injection hole suppresses cavitation, and cavitation inside the nozzle disappears completely when the injection hole cone angle is less than −10°. [ABSTRACT FROM AUTHOR]

The objective of the proposed research is to develop plasma soft X-ray (SXR) radiation imaging that includes spectral information in addition to standard SXR tomography for the purpose of studying, for example, tungsten transport and its interplay with magnetohydrodynamics (MHD) in tokamak plasmas in an ITER-relevant approach. The SXR radiation provides valuable information about both aspects, particularly when measured with high spatial and temporal resolution and when tomographic reconstructions are performed. The spectral data will facilitate the tracking of both light and high-Z impurities. This approach is pertinent to both the advancement of a detailed understanding of physics and the real-time control of plasma, thereby preventing radiative collapses. The significance of this development lies in its ability to provide three-dimensional plasma tomography, a capability that extends beyond the scope of conventional tomography. The utilization of two-dimensional imaging capabilities inherent to Gas Electron Multiplier (GEM) detectors in a toroidal view, in conjunction with the conventional poloidal tomography, allows for the acquisition of three-dimensional information, which should facilitate the study of, for instance, the interplay between impurities and MHD activities. Furthermore, this provides a valuable opportunity to investigate the azimuthal asymmetry of tokamak plasmas, a topic that has rarely been researched. The insights gained from this research could prove invaluable in understanding other toroidal magnetically confined plasmas, such as stellarators, where comprehensive three-dimensional measurements are essential. To illustrate, by attempting to gain access to anisotropic radiation triggered by magnetic reconnection or massive gas injections, such diagnostics will provide the community with enhanced experimental tools to understand runaway electrons (energy distribution and spatial localization) and magnetic reconnection (spatial localization, speed...). This work forms part of the optimization studies of a detecting unit proposed for use in such a diagnostic system, based on GEM technology. The detector is currently under development with the objective of achieving the best spatial resolution feasible with this technology (down to approximately 100 µm). The diagnostic design focuses on the monitoring of photons within the 2–15 keV range. The findings of the optimization studies conducted on the amplification stage of the detector, particularly with regard to the geometrical configuration of the GEM foils, are presented herein. The impact of hole shape and spacing in the amplifying foils on the detector parameters, including the spatial size of the avalanches and the electron gain/multiplication, has been subjected to comprehensive numerical analysis through the utilization of Degrad (v. 3.13) and Garfield++ (v. bd8abc76) software. The results obtained led to the identification of two configurations as the most optimal geometrical configurations of the amplifying foil for the three-foil GEM system for the designed detector. The first configuration comprises cylindrical holes with a diameter of 70 μm, while the second configuration comprises biconical holes with diameters of 70/50/70 μm. Both configurations had a hole spacing of 120 μm. [ABSTRACT FROM AUTHOR]

Background/Objectives: Radiography is an essential and low-cost diagnostic method in pulmonary medicine that is used for the early detection and monitoring of lung diseases. An adequate and consistent image quality (IQ) is crucial to ensure accurate diagnosis and effective patient management. This pilot study evaluates the feasibility and effectiveness of the International Atomic Energy Agency (IAEA)'s remote and automated quality control (QC) methodology, which has been tested in multiple imaging centers. Methods: The data, collected between April and December 2022, included 47 longitudinal data sets from 22 digital radiographic units. Participants submitted metadata on the radiography setup, exposure parameters, and imaging modes. The database comprised 968 exposures, each representing multiple image quality parameters and metadata of image acquisition parameters. Python scripts were developed to collate, analyze, and visualize image quality data. Results: The pilot survey identified several critical issues affecting the future implementation of the IAEA method, as follows: (1) difficulty in accessing raw images due to manufacturer restrictions, (2) variability in IQ parameters even among identical X-ray systems and image acquisitions, (3) inconsistencies in phantom construction affecting IQ values, (4) vendor-dependent DICOM tag reporting, and (5) large variability in SNR values compared to other IQ metrics, making SNR less reliable for image quality assessment. Conclusions: Cross-comparisons among radiography systems must be taken with cautious because of the dependence on phantom construction and acquisition mode variations. Awareness of these factors will generate reliable and standardized quality control programs, which are crucial for accurate and fair evaluations, especially in high-frequency chest imaging. [ABSTRACT FROM AUTHOR]

Modern aviation security systems are largely tied to the work of screening operators. Due to physical characteristics, they are prone to problems such as fatigue, loss of attention, etc. There are methods for recognizing such objects, but they face such difficulties as the specific structure of luggage X-ray images. Furthermore, such systems require significant computational resources when increasing the size of models. Overcoming the first and second disadvantage can largely lie in the hardware plane. It needs new introscopes and registration techniques, as well as more powerful computing devices. However, for processing, it is more preferable to improve quality without increasing the computational power requirements of the recognition system. This can be achieved on traditional neural network architectures, but with the more complex training process. A new training approach is proposed in this study. New ways of baggage X-ray image augmentation and advanced approaches to training convolutional neural networks and vision transformer networks are proposed. It is shown that the use of ArcFace loss function for the task of the items binary classification into forbidden and allowed classes provides a gain of about 3–5% for different architectures. At the same time, the use of softmax activation function with temperature allows one to obtain more flexible estimates of the probability of belonging, which, when the threshold is set, allows one to significantly increase the accuracy of recognition of forbidden items, and when it is reduced, provides high recall of recognition. The developed augmentations based on doubly stochastic image models allow one to increase the recall of recognizing dangerous items by 1–2%. On the basis of the developed classifier, the YOLO detector was modified and the mAP gain of 0.72% was obtained. Thus, the research results are matched to the goal of increasing efficiency in X-ray baggage image processing. [ABSTRACT FROM AUTHOR]

Introduction: Convolutional Neural Network (CNN) systems in healthcare are influenced by unbalanced datasets and varying sizes. This article delves into the impact of dataset size, class imbalance, and their interplay on CNN systems, focusing on the size of the training set versus imbalance—a unique perspective compared to the prevailing literature. Furthermore, it addresses scenarios with more than two classification groups, often overlooked but prevalent in practical settings. Methods: Initially, a CNN was developed to classify lung diseases using X-ray images, distinguishing between healthy individuals and COVID-19 patients. Later, the model was expanded to include pneumonia patients. To evaluate performance, numerous experiments were conducted with varied data sizes and imbalance ratios for both binary and ternary classifications, measuring various indices to validate the model's efficacy. Results: The study revealed that increasing dataset size positively impacts CNN performance, but this improvement saturates beyond a certain size. A novel finding is that the data balance ratio influences performance more significantly than dataset size. The behavior of three-class classification mirrored that of binary classification, underscoring the importance of balanced datasets for accurate classification. Conclusions: This study emphasizes the fact that achieving balanced representation in datasets is crucial for optimal CNN performance in healthcare, challenging the conventional focus on dataset size. Balanced datasets improve classification accuracy, both in two-class and three-class scenarios, highlighting the need for data-balancing techniques to improve model reliability and effectiveness. Motivation: Our study is motivated by a scenario with 100 patient samples, offering two options: a balanced dataset with 200 samples and an unbalanced dataset with 500 samples (400 healthy individuals). We aim to provide insights into the optimal choice based on the interplay between dataset size and imbalance, enriching the discourse for stakeholders interested in achieving optimal model performance. Limitations: Recognizing a single model's generalizability limitations, we assert that further studies on diverse datasets are needed. [ABSTRACT FROM AUTHOR]

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network's determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region of interest (ROI) cropping, and data augmentation steps to enhance classification accuracy. Later, a multi-layer Gradient CAM (ML-Grad-CAM) algorithm is integrated to generate a class-specific saliency map for improved visualization in CXR images. We also define and calculate a Severity Assessment Index (SAI) from the saliency map to quantitatively measure infection severity. The trained model achieved an accuracy score of 96.44% for the three-class CXR classification task, i.e., COVID-19, pneumonia, and normal (healthy patients), outperforming many existing techniques in the literature. The saliency maps generated from the proposed ML-GRAD-CAM algorithm are compared with the original Gran-CAM algorithm. [ABSTRACT FROM AUTHOR]

Organic thermally activated delayed fluorescent (TADF) scintillators hold promising potential for applications in medical radiography and security detection, but the poor X‐ray absorption ability and inferior radioluminescence (RL) hampered their progression. Herein, the study has pioneered the development of high‐performance TADF Ag(I)‐based scintillators from M2X2(dppb)2 (M = Ag, Cu; X = Cl, Br, I) complexes with 1,2‐Bis(diphenylphosphino)benzene (dppb) ligand. In comparison with Cu(I) complexes, the Ag(I) series generally exhibited superior scintillation performance. Notably, Ag2Cl2(dppb)2 (Ag1) stands out with exceptionally high RL intensity (≈125% higher than that of CsI:Tl) and a low detection limit of 59.8 nGy s−1. The outstanding scintillation performance of Ag1 is primarily attributed to the synergistic effect of the high exciton utilization efficiency origin from a small singlet‐triplet energy gap, enhanced X‐ray absorption capacity by heavy atoms, and the high photoluminescence quantum yield (76.47% in ambient atmosphere). By fabricating a flexible film constructed with Ag1 submicron crystalline powders, a high spatial resolution of 25.0 lp mm−1 for X‐ray imaging is obtained. It offers new opportunities for utilizing TADF metal–organic complexes for highly efficient X‐ray scintillation and imaging. [ABSTRACT FROM AUTHOR]

The paper considers the deformation behavior of a cylindrical shell made of industrial wrought magnesium alloy MA-14 (Mg 93 wt %–Zn 5–6 wt %–Zr 0.3–0.9 wt %) loaded by a method of sliding detonation of a laid-on explosive uniformly placed on the outer surface of the shell. The convergence scenario of the magnesium alloy shell is analyzed on the basis of the X-ray data. The methods of optical and scanning electron microscopy are used to investigate the evolution of the structure at high-rate deformation. The hardness is measured along a radius and over the length of the shell. [ABSTRACT FROM AUTHOR]

X-ray security images face significant challenges due to complex backgrounds, item overlap, and multi-scale target detection. Traditional methods often struggle to accurately identify objects, especially under cluttered conditions. This paper presents an advanced detection model, called YOLOv8n-GEMA, which incorporates several enhancements to address these issues. Firstly, the generalized efficient layer aggregation network (GELAN) module is employed to augment the feature fusion capabilities. Secondly, to tackle the problems of overlap and occlusion in X-ray images, the efficient multi-scale attention (EMA) module is utilized, effectively managing the feature capture and interdependencies among overlapping items, thereby boosting the model's detection capability in such scenarios. Lastly, addressing the diverse sizes of items in X-ray images, the Inner-CIoU loss function uses auxiliary bounding boxes at varying scale ratios for loss calculation, ensuring faster and more effective bounding box predictions. The enhanced YOLOv8 model was tested on the public datasets SIXRay, HiXray, CLCXray, and PIDray, where the improved model's mean average precision (mAP) reached 94.4%, 82.0%, 88.9%, and 85.9%, respectively, showing improvements of 3.6%, 1.6%, 0.9%, and 3.4% over the original YOLOv8. These results demonstrate the effectiveness and universality of the proposed method. Compared to current mainstream X-ray images of dangerous goods detection models, this model significantly reduces the false detection rate of dangerous goods in X-ray security images and achieves substantial improvements in the detection of overlapping and multi-scale targets, realizing higher accuracy in dangerous goods detection. [ABSTRACT FROM AUTHOR]

Summary: Pneumonia disease progress rapidly leading to missed diagnosis and diagnosis error. However, it is significant to be diagnosed accurately as well as adopted timely treatments, particularly for children and elderly people over 65. In the first place, we propose a novel method for data augmentation to expand non‐case samples in order to solve the problem of imbalance of the raw data. Then we have designed a neural network based on separable convolution layers, using deep learning to classify between Pneumonia cases and normal cases from chest X‐ray images. Finally, well‐known performance measures are utilized to evaluate our model, obtaining the following results: Accuracy (0.97), AUC (0.99), Sensitivity (0.98), Specificity (0.98), Precision (0.95), F1‐Score (0.97), and Youden Index (0.90). Simultaneously, we highlighted the proposed neural network recognition area via Class Activation Method for the purpose of providing an explainable diagnosis. Compared with the‐state‐of‐art models, our model performed better, and it may be regarded as an alternative in countries or regions lack of the relevant equipment and professional radiologists. [ABSTRACT FROM AUTHOR]

This research project aims to automate the identification, labeling, and counting of teeth, as well as the classification of abnormalities and detection of caries in dental X-rays, specifically orthopantomograms (OPGs). It involves several deep neural networks and learning algorithms. The first module uses semantic segmentation with a U-net model to create masks for tooth detection, which are then refined with the YOLOv3 detector, achieving 80% accuracy. Canonical correlation analysis (CCA) helps find tooth midpoints and count the total number of teeth. The second module classifies abnormalities and pathologies using transfer learning with the Inceptionv3 model, yielding moderate accuracy. Caries detection is performed with thresholding and segmentation. The third module detects three treated pathologies—root canal treatments, crowns, and implants—using Faster RCNN and Inceptionv3, showing fair accuracy. Overall, the automated approach demonstrates promising results for enhancing X-ray image interpretation and diagnosing oral diseases. [ABSTRACT FROM AUTHOR]

A recent global health crisis, COVID-19 is a significant global health crisis that has profoundly affected lifestyles. The detection of such diseases from similar thoracic anomalies using medical images is a challenging task. Thus, the requirement of an end-to-end automated system is vastly necessary in clinical treatments. In this way, the work proposes a Squeeze-and-Excitation Attention-based ResNet50 (SEA-ResNet50) model for detecting COVID-19 utilizing chest X-ray data. Here, the idea lies in improving the residual units of ResNet50 using the squeeze-and-excitation attention mechanism. For further enhancement, the Ranger optimizer and adaptive Mish activation function are employed to improve the feature learning of the SEA-ResNet50 model. For evaluation, two publicly available COVID-19 radiographic datasets are utilized. The chest X-ray input images are augmented during experimentation for robust evaluation against four output classes namely normal, pneumonia, lung opacity, and COVID-19. Then a comparative study is done for the SEA-ResNet50 model against VGG-16, Xception, ResNet18, ResNet50, and DenseNet121 architectures. The proposed framework of SEA-ResNet50 together with the Ranger optimizer and adaptive Mish activation provided maximum classification accuracies of 98.38% (multiclass) and 99.29% (binary classification) as compared with the existing CNN architectures. The proposed method achieved the highest Kappa validation scores of 0.975 (multiclass) and 0.98 (binary classification) over others. Furthermore, the visualization of the saliency maps of the abnormal regions is represented using the explainable artificial intelligence (XAI) model, thereby enhancing interpretability in disease diagnosis. [ABSTRACT FROM AUTHOR]

In clinical X-ray imaging, a focused grid is used to enhance image contrast by removing scattered radiation. The focused grid removes scattered radiation only when the X-ray source is located in the focal point of the grid. In oblique imaging, the grid cuts off some of the primary radiation because the X-ray radiation is not aligned with the lead strips. If the tilting angle of the grid elements and the direction of X-ray radiation are not aligned, it results in less X-ray reaching the imaging detector. Therefore, in oblique imaging cases, the exposure condition is generally increased to obtain a clinically valuable image. In this paper, we propose a method to acquire proper image quality in terms of both contrast and signal-to-noise ratio (SNR) in oblique X-ray imaging by positioning the subject in a specific grid region where the X-ray projection is aligned with the tilted angle of the grid lead strips. We examined experimental imaging with several types of radiographic phantoms, and the results demonstrate the feasibility of the proposed method. This study is applicable to non-standard X-ray imaging cases involving patients who are difficult to position correctly in oblique views. In such cases, the proposed approach sets the direction of X-ray radiation without requiring patient movement. [ABSTRACT FROM AUTHOR]

The present panic issue all over world is COVID-19. Most perfect with quick identification of COVID-19 is required to do superior decision that provides immediate treatment for the affected individuals and protect from death. Nevertheless, extracting the affected areas from the chest X-ray images contains many issues like it depends on the characteristics of the infection, less intensity between the normal and affected tissues. The major aim of the work is to design COVID-19 X-ray segmentation through Thresholding Chaotic Butterfly Optimization Algorithm with Gaussian Kernel (TCBOAGK) algorithm is introduced to detect the affected areas in X-ray images automatically. However, in the TCBOAGK, initially images are spitted via the MapReduce (MR) Model. Thus reduces the complexity of the algorithm, in each map similar COVID area is calculated via the Gaussian kernel. The searching of the similar pixels related to COVID or not is performed via the TCBOAGK with Gaussian kernel objective function. Regions are detected by the TCBOAGK algorithm which is used for similarity computation. The similarity of the regions between the produced COVID and the COVID is derived from the X-ray images via the Gaussian kernel. After which, DeTrac (Decompose, Transfer, and Compose) classifier is proposed to classify the images that are segmented. The purpose of using class decomposition is to improve the classifier with less variance that helps to improve their flexibility in deciding the boundaries. It also helps in simplifying the local structure of database that can handle irregularities that are present in the distribution of data. Outcome of the analysis indicated the DeTraC potential in identifying COVID-19 individuals through complete X-ray image dataset obtained from all over the world. Five widely adopted metrics, i.e., the Accuracy, Correctness, Completeness, Specificity, F1 score and MCC-Matthew Correlation Coefficient have been used for evaluation. [ABSTRACT FROM AUTHOR]

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

X‐ray imaging serves a critical role across diverse sectors, including medical diagnostics, industrial surveillance, security, and space exploration. This study delves into the unique properties of a novel polar crystal, [FMPPA]ZnBr4 (FMPPA = protonated 3‐fluoro‐4‐(4‐methyl‐1‐piperazinyl)aniline), characterized by a 0D metal halide structure. This crystal features [ZnBr4]2− tetrahedra and A‐site organic amine ion [FMPPA]2+ rings. With a high photoluminescence quantum yield (≈70%) and minimal self‐absorption, [FMPPA]ZnBr4 demonstrates exceptional scintillation performance under X‐ray excitation, producing 29300 ± 600 photons MeV−1 and achieving a low detection limit of 0.352 µGyair s−1, surpassing conventional 3D perovskite nanocrystals. Beyond superior performance, this scintillator meets the demand for efficient X‐ray imaging light emitters. Moreover, it consistently achieves ≈12.5 lp mm−1 high‐resolution X‐ray imaging. Additionally, [FMPPA]ZnBr4 exhibits a nonlinear optical response, surpassing the benchmark KH2PO4 by 1.8 times. This investigation introduces a new structure framework, paving the way for the development of integrated optoelectronic materials with both nonlinear and linear optical properties. [ABSTRACT FROM AUTHOR]

A position-sensitive spectrometer with subpixel spatial resolution on the basis of a CCD camera has been implemented. The spectrometer operation is based on the proposed algorithm for real-time analysis of frames with single photon-induced events. The factors affecting the energy resolution, formation of artifacts in energy spectra, and count efficiency are analyzed. An algorithm for obtaining subpixel resolution with application of singular value decomposition is proposed. The algorithm operation has been tested on synthesized experimental data and in a live experiment with registration of the spatial and energy structure of X-ray fluorescence emission from a layered system. The application potential of hyperspectral imaging for the experimental method of X-ray standing waves in the geometry of normal incidence and grazing X-ray fluorescence emission is demonstrated. [ABSTRACT FROM AUTHOR]

HXI on ASO-S and STIX onboard Solar Orbiter are the first simultaneously operating solar hard X-ray imaging spectrometers. ASO-S's low Earth orbit and Solar Orbiter's periodic displacement from the Sun–Earth line enables multi-viewpoint solar hard X-ray spectroscopic imaging analysis for the first time. Here, we demonstrate the potential of this new capability by reporting the first results of 3D triangulation of hard X-ray sources in the SOL2023-12-31T21:55 X5 flare. HXI and STIX observed the flare near the east limb with an observer separation angle of 18°. We triangulated the brightest regions within each source, which enabled us to characterise the large-scale hard X-ray geometry of the flare. The footpoints were found to be in the chromosphere within uncertainty, as expected, while the thermal looptop source was centred at an altitude of 15.1 ± 1 Mm. Given the footpoint separation, this implies a more elongated magnetic-loop structure than predicted by a semi-circular model. These results show the strong diagnostic power of joint HXI and STIX observations for understanding the 3D geometry of solar flares. We conclude by discussing the next steps required to fully exploit their potential. [ABSTRACT FROM AUTHOR]

In this study, the DiagCovidPNA method is proposed to aid in diagnosing and differentiating COVID-19 disease and viral and bacterial pneumonia (PNA) from chest X-ray (CXR) images. The proposed method is based on application-specific convolutional neural network (CNN) architectures. DiagCovidPNA is a four-class (i.e., COVID-19, normal lung, bacterial PNA, and viral PNA) diagnostic method that is offered in two different systems: (1) DiagCovidPNA and (2) Hybrid DiagCovidPNA. In the first system, a single CNN is used to classify the input images into four classes. In the second system, to further differentiate bacterial and viral PNAs more, the input images are classified into four classes using a combination of a three-class (i.e., COVID-19, normal lung, PNA) CNN-based classifier and a two-class (i.e., bacterial PNA and viral PNA) CNN-based classifier. A comprehensive labeled database of CXR images for training and testing purposes has been compiled using two sources of available images. In addition, the transfer learning method is applied to reduce the issue of insufficient training data. Extensive experiments are conducted to evaluate and compare both systems to existing methods. According to the results, the diagnostic accuracy of DiagCovidPNA and Hybrid DiagCovidPNA is 95% and 97.50%, respectively. Consequently, Hybrid DiagCovidPNA improves accuracy by approximately 8% compared to other deep learning-based methods with a same structure and approximately 3% compared to the parallel transfer deep learning-based method. All DiagCovidPNA implementation source codes and the compiled CXR database are publicly available to the research community. [ABSTRACT FROM AUTHOR]

Portland cements (PCs) and cement blends are multiphase materials of different fineness, and quantitatively analysing their hydration pathways is very challenging. The dissolution (hydration) of the initial crystalline and amorphous phases must be determined, as well as the formation of labile (such as ettringite), reactive (such as portlandite) and amorphous (such as calcium silicate hydrate gel) components. The microstructural changes with hydration time must also be mapped out. To address this robustly and accurately, an innovative approach is being developed based on in situ measurements of pastes without any sample conditioning. Data are sequentially acquired by Mo Kα1 laboratory X‐ray powder diffraction (LXRPD) and microtomography (µCT), where the same volume is scanned with time to reduce variability. Wide capillaries (2 mm in diameter) are key to avoid artefacts, e.g. self‐desiccation, and to have excellent particle averaging. This methodology is tested in three cement paste samples: (i) a commercial PC 52.5 R, (ii) a blend of 80 wt% of this PC and 20 wt% quartz, to simulate an addition of supplementary cementitious materials, and (iii) a blend of 80 wt% PC and 20 wt% limestone, to simulate a limestone Portland cement. LXRPD data are acquired at 3 h and 1, 3, 7 and 28 days, and µCT data are collected at 12 h and 1, 3, 7 and 28 days. Later age data can also be easily acquired. In this methodology, the amounts of the crystalline phases are directly obtained from Rietveld analysis and the amorphous phase contents are obtained from mass‐balance calculations. From the µCT study, and within the attained spatial resolution, three components (porosity, hydrated products and unhydrated cement particles) are determined. The analyses quantitatively demonstrate the filler effect of quartz and limestone in the hydration of alite and the calcium aluminate phases. Further hydration details are discussed. [ABSTRACT FROM AUTHOR]

Background: While cardiopulmonary resuscitation (CPR) may be life-saving for patients in cardiac arrest, it simultaneously puts them at risk for skeletal and soft tissue injuries. The prevalence of cardiovascular and thoracic wall injuries related to CPR varies significantly in the literature, from 21% to more than 78%. After restoration of circulation, ischemia–reperfusion brain injury ensues. Sedation is one of the interventions that can reduce its effects on brain cells. The purpose of this study was to analyse the use of analgesics and sedatives in nontraumatic patients after sudden cardiac arrest in the peri-resuscitation period in the emergency department. Methods: This was a retrospective cohort study. An analysis was performed on the electronic health records of 131 patients who underwent CPR, with ages ranging from 24 to 96 years. The study protocol was in accordance with the Declaration of Helsinki. Results: Chest wall injuries were assessed based on the results of X-ray imaging (n = 39; 31%) and computed tomography (n = 87; 69%). Of the 126 cases, 17.5% had rib fractures and 6.3% had rib and sternal fractures. Almost 78% of the patients (n = 102) received sedatives and/or analgesics during the peri-resuscitation period. Monotherapy was used in 85 cases. Among these drugs the most frequently mentioned were midazolam (45.2%), fentanyl (26.8%), and propofol (20.8%). Conclusions: As only two-thirds of the patients received sedation and half received analgesics, there is still room for a broader incorporation of analgesia and sedation into peri-resuscitation care protocols. [ABSTRACT FROM AUTHOR]

Background: The recent emergence of targeted radionuclide therapy has increased the demand for imagers capable of visualizing pharmacokinetics in developing radiopharmaceuticals in the preclinical phase. Some radionuclides emit hard x‐rays and gamma‐rays below 100 keV, in which energy range the performance of conventional NaI scintillators is poor. Multipinhole collimators are also used for small animal imaging with a good spatial resolution but have a limited field of view (FOV). Purpose: In this study, a new imager with high sensitivity over a wide FOV in the low‐energy band (<$<$100 keV) was developed for the pharmacokinetic study. Methods: We developed an x‐ray and gamma‐ray camera for high‐resolution spectroscopy, named "CdTe XG‐Cam," equipped with a cadmium telluride semiconductor detector and a parallel‐hole collimator using a metal 3D printer. To evaluate the camera‐system performance, phantom measurements with single and dual nuclides (99mTc$^{\rm 99m}{\rm Tc}$, 111In$^{111}{\rm In}$, and 125I)$^{125}{\rm I)}$ were performed. The performance for in vivo imaging was evaluated using tumor‐bearing mice to which a nuclide (99mTc$^{\rm 99m}{\rm Tc}$ or 125I)$^{125}{\rm I)}$ administered. Results: We simultaneously obtained information on 111In$^{111}{\rm In}$ and 125I$^{125}{\rm I}$, which emit emission lines in the low‐energy band with peak energies close to each other (23–26 keV for 111In$^{111}{\rm In}$ and 27–31 keV for 125I)$^{125}{\rm I)}$, and applied an analytical method based on spectral model fitting to determine the individual radioactivities accurately. In the small animal imaging, the distributions of the nuclide in tumors were accurately quantified and time‐activity curves in tumors are obtained. Conclusions: The demonstrated capability of our system to perform in vivo imaging suggests that the camera can be used for applications of pharmacokinetics research. [ABSTRACT FROM AUTHOR]

Guidelines generally recommend a combination of immunological assays and chest X-ray imaging (CXR) when screening for latent tuberculosis infection (LTBI) prior to biologic treatment in inflammatory bowel disease (IBD). To investigate whether CXR identify patients with suspected LTBI/TB who were not identified with QuantiFERON tests (QFT) when screening for LTBI/TB before starting biologic treatment in IBD patients. Single-center, retrospective cohort study of patients with inflammatory bowel disease who had a QFT and a CXR prior to initiation of biologic treatment in a 5-year period (October 1st, 2017 to September 30th, 2022). 520 patients (56% female, mean age 40.1 years) were included. The majority had none or few risk factors for TB (as reflected by the demographic characteristics) but some risk factors for having false negative QFT results (concurrent glucocorticoid treatment and inflammatory activity). QFT results were positive in 8 patients (1.5%), inconclusive in 18 (3.5%) and negative in 494 (95.0%). Only 1 patient (0.19%) had CXR findings suspicious of LTBI. This patient also had a positive QFT and was subsequently diagnosed with active TB. All patients with negative or inconclusive QFT had CXR without any findings suggesting LTBI/TB. One patient developed active TB after having initiated biologic treatment in spite of having negative QFT and a normal CXR at screening. In a population with low risk of TB, the benefits of supplementing the QFT with a CXR are limited and are unlikely to outweigh the cost in both patient test-burden, radioactive exposure, and economic resources. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of element size on the microstructure of the steel–concrete interface in self-compacting concrete (SCC). Experiments were conducted on two types of test elements: a deep beam measuring 1440 × 640 × 160 mm and a wall element measuring 2240 × 1600 × 160 mm. The SCC mix was consistently pumped from the top, using a single casting point located near the formwork's edge. Horizontal steel ribbed rebars with a diameter of 16 mm were embedded in these elements. X-ray computed tomography (CT) was employed to provide three-dimensional insights into the microstructure of the rebar-to-concrete interface. An analysis of X-ray CT images from core samples revealed that the microstructure of this interface is influenced by the distance of the specimen from the mix casting point and its vertical position within the element. The combined effects of bleeding, air-pore entrapment, and plastic settlement within the SCI were observed under the top rebars. Their extent was independent of the type of element analyzed, suggesting that the deterioration of the SCI is related to the distance from the top surface of the element. These results elucidate phenomena occurring during the fresh state of concrete near reinforcing bars and their implications for bond properties. To date, some of the standards differentiate between bond conditions according to the depth of concrete beneath the rebar. In the view of the studies, this approach may be unduly rigorous. The findings offer valuable guidance for reinforced concrete execution and design. [ABSTRACT FROM AUTHOR]

Our research evaluates advanced artificial (AI) methodologies to enhance diagnostic accuracy in pulmonary radiography. Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model's performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings. [ABSTRACT FROM AUTHOR]

Clusters of galaxies are merging during the formation of large-scale structures in the Universe. Based on optical survey data, we identify a large sample of pre-mergers of galaxy clusters and merging subclusters in rich clusters. We find 39 382 partners within a velocity difference of 1500 km s |$^{-1}$| and a projected separation of 5  |$r_{500}$| around 33 126 main clusters, where |$r_{500}$| is the radius of the main cluster. Based on the galaxy distribution inside rich clusters with more than 30 member galaxy candidates, we identify subclusters by modelling the smoothed optical distribution with a two-component profile, and a coupling factor is obtained for merging subclusters in 7845 clusters. In addition, we find 3446 post-collision mergers according to the deviations of brightest cluster galaxies from other member galaxies, most of which have been partially validated by using the Chandra and XMM–Newton X-ray images. Two new bullet-like clusters have been identified by using the optical and X-ray images. The large samples of merging clusters of galaxies presented here are important databases for studying the hierarchical structure formation, cluster evolution, and the physics of intergalactic medium. [ABSTRACT FROM AUTHOR]

To regulate the effective photon energy with a constant tube voltage and to perform enhanced K-edge x-ray imaging, we built a prototype fully digitalized triple-energy x-ray computed tomography (TECT) scanner utilizing beam hardening. An object on the turntable is irradiated by a 0.1-mm-focus x-ray tube at 1.4-time-magnification. 720 raw radiograms are obtained using a flat panel detector, and 512 tomograms are reconstructed. With an increasing digital amplification factor, the gray raw-density projections obtained using low-energy-photon absorption disappeared at a constant maximum raw density, and the effective energy was increased by x-ray beam hardening. TECT was performed at digital amplifier factors below 3.0, and the effective energy increased with increasing amplification factors. In particular, fine blood vessels were visible using gadolinium-K-edge CT. [ABSTRACT FROM AUTHOR]

Hardened gated x-ray detectors use photographic film as the data recording medium due to its low sensitivity to the high-yield neutron environments at the National Ignition Facility (NIF). The photographic film is digitized with a Photometric Data Systems (PDS) microdensitometer, which measures the film's optical density. The PDS scanner is able to measure a dynamic range of 0–5 OD; however, raster scanning the film is time consuming and maintenance of the instrument is challenging due to legacy technology. Since film usage at NIF is expected to continue in the foreseeable future, a digitization platform that is faster and more maintainable would benefit the NIF's current and future operations. This work presents the characterization of the digital transitions (DT) atom, a CMOS camera-based digitization platform that records film data in a single image capture very quickly and has widely available user support. The preliminary results suggest that the DT atom is able to reconstruct exposures accurately enough to be a competitive alternative to the PDS Scanner. [ABSTRACT FROM AUTHOR]

X‐ray security checks aim to detect contraband in luggage; however, the detection accuracy is hindered by the overlapping and significant size differences of objects in X‐ray images. To address these challenges, the authors introduce a novel network model named Multi‐Scale Feature Attention (MSFA)‐DEtection TRansformer (DETR). Firstly, the pyramid feature extraction structure is embedded into the self‐attention module, referred to as the MSFA. Leveraging the MSFA module, MSFA‐DETR extracts multi‐scale feature information and amalgamates them into high‐level semantic features. Subsequently, these features are synergised through attention mechanisms to capture correlations between global information and multi‐scale features. MSFA significantly bolsters the model's robustness across different sizes, thereby enhancing detection accuracy. Simultaneously, A new initialisation method for object queries is proposed. The authors' foreground sequence extraction (FSE) module extracts key feature sequences from feature maps, serving as prior knowledge for object queries. FSE expedites the convergence of the DETR model and elevates detection accuracy. Extensive experimentation validates that this proposed model surpasses state‐of‐the‐art methods on the CLCXray and PIDray datasets. [ABSTRACT FROM AUTHOR]

The Advanced X-ray Imaging Satellite (AXIS) promises revolutionary science in the X-ray and multi-messenger time domain. AXIS will leverage excellent spatial resolution (<1.5 arcsec), sensitivity ( 80 × that of Swift), and a large collecting area (5–10× that of Chandra) across a 24-arcmin diameter field of view at soft X-ray energies (0.3–10.0 keV) to discover and characterize a wide range of X-ray transients from supernova-shock breakouts to tidal disruption events to highly variable supermassive black holes. The observatory's ability to localize and monitor faint X-ray sources opens up new opportunities to hunt for counterparts to distant binary neutron star mergers, fast radio bursts, and exotic phenomena like fast X-ray transients. AXIS will offer a response time of <2 h to community alerts, enabling studies of gravitational wave sources, high-energy neutrino emitters, X-ray binaries, magnetars, and other targets of opportunity. This white paper highlights some of the discovery science that will be driven by AXIS in this burgeoning field of time domain and multi-messenger astrophysics. This White Paper is part of a series commissioned for the AXIS Probe Concept Mission; additional AXIS White Papers can be found at the AXIS website. [ABSTRACT FROM AUTHOR]

Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interpatient, and intra-patient. The paper explores the challenges encountered in medical image registration, including geometric distortion, differences in image properties, outliers, and optimization convergence, and discusses their impact on registration accuracy and reliability. Strategies for addressing these challenges are highlighted, emphasizing the need for continual innovation and refinement of techniques to enhance the accuracy and reliability of medical image registration systems. The paper concludes by emphasizing the importance of accurate medical image registration in improving diagnosis. [ABSTRACT FROM AUTHOR]