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We describe a method to compute photon–matter interaction and atomic dynamics with x-ray lasers using a hybrid code based on classical molecular dynamics and collisional-radiative calculations. The forces between the atoms are dynamically determined based on changes to their electronic occupations and the formation of a free electron cloud created from the irradiation of photons in the x-ray spectrum. The rapid transition from neutral solid matter to dense plasma phase allows the use of screened potentials, reducing the number of non-bonded interactions. In combination with parallelization through domain decomposition, the hybrid code handles large-scale molecular dynamics and ionization. This method is applicable for large enough samples (solids, liquids, proteins, viruses, atomic clusters, and crystals) that, when exposed to an x-ray laser pulse, turn into a plasma in the first few femtoseconds of the interaction. We present four examples demonstrating the applicability of the method. We investigate the non-thermal heating and scattering of bulk water and damage-induced dynamics of a protein crystal using an x-ray pump–probe scheme. In both cases, we compare to the experimental data. For single particle imaging, we simulate the ultrafast dynamics of a methane cluster exposed to a femtosecond x-ray laser. In the context of coherent diffractive imaging, we study the fragmentation as given by an x-ray pump–probe setup to understand the evolution of radiation damage in the time range of hundreds of femtoseconds. [ABSTRACT FROM AUTHOR]

Dual-energy imaging represents a versatile and evolving technology with wide-ranging applications in medicine and beyond. Recent technological developments increased the potential for improved diagnostic accuracy and expanded imaging capabilities across various fields. The purpose of this work is to design and develop an energy-integrating multilayer detector, known as a sandwich detector, aimed at single-shot dual-energy imaging tasks such as material discrimination and contrast cancellation. The sandwich detector uses two complementary metal oxide semiconductor advanced pixel sensors of 50 μm pixel size. The top and bottom sensors detect low-energy (LE) and high-energy (HE) photons, with sensors coupled with 250 and 600 μm scintillators, respectively. For better spectral separation between layers without excessively affecting the detected statistic in the bottom layer, the insertion of a 0.25-mm Cu filter between the layers was found to be the optimal choice, from among the tested 0-, 0.25-, and 0.5-mm filter options. The thickness selection for scintillator and intermediate Cu filter was carried out through a dual-energy simulation model. The experiments confirmed the model's reliability in selecting the optimal thicknesses of the intermediate Cu filter, thereby providing reassurance also on the choice of the top scintillator. [ABSTRACT FROM AUTHOR]

The ability to visualize x-ray and neutron emission from fusion plasmas in 3D is critical to understand the origin of the complex shapes of the plasmas in experiments. Unfortunately, this remains challenging in experiments that study a fusion concept known as Magnetized Liner Inertial Fusion (MagLIF) due to a small number of available diagnostic views. Here, we present a basis function-expansion approach to reconstruct MagLIF stagnation plasmas from a sparse set of x-ray emission images. A set of natural basis functions is "learned" from training volumes containing quasi-helical structures whose projections are qualitatively similar to those observed in experimental images. Tests on several known volumes demonstrate that the learned basis outperforms both a cylindrical harmonic basis and a simple voxel basis with additional regularization, according to several metrics. Two-view reconstructions with the learned basis can estimate emission volumes to within 11% and those with three views recover morphology to a high degree of accuracy. The technique is applied to experimental data, producing the first 3D reconstruction of a MagLIF stagnation column from multiple views, providing additional indications of liner instabilities imprinting onto the emitting plasma. [ABSTRACT FROM AUTHOR]

Pulsed-power-driven underwater electrical explosion of cylindrical or conical wire arrays produces supersonic water jets that emerge from a bath, propagating through the air above it. Interaction of these jets with solid targets may represent a new platform for attaining materials at high pressure (>1010 Pa) conditions in a university-scale laboratory. However, measurements of the internal structure of such jets and how they interact with targets are difficult optically due to large densities and density contrasts involved. We utilized multi-frame x-ray radiographic imaging capabilities of the ID19 beamline at the European Synchrotron Radiation Facility to explore the water jet and its interaction with a 50 μm thick copper foil placed a few mm from the surface of water. The jet was generated with a ∼130 kA-amplitude current pulse of ∼450 ns rise time applied to a conical wire array. X-ray imaging revealed a droplet-type structure of the jet with an average density of <400 kg/m3 propagating with a velocity of ∼1400 m/s. Measurements of deformation and subsequent perforation of the target by the jet suggested pressures at the jet–target interface of ∼5 × 109 Pa. The results were compared to hydrodynamic simulations for better understanding of the jet parameters and their interaction with the foil target. These results can be used in future research to optimize the platform, and extend it to larger jet velocities in the case of higher driving currents supplied to the wire array. [ABSTRACT FROM AUTHOR]

X‐ray imaging technology has attracted widespread attention due to its important application in the industrial and medical fields. The practical‐level X‐ray imager requires high‐performance scintillators with high light yield, low detection limit, superb stability, and excellent processibility. Here, a Mn2+‐doped 2D Dion‐Jacobson perovskite scintillator of (HIS)PbCl4 (HIS2+ is histammonium), is presented serving as a potential candidate for high‐resolution X‐ray imaging. By facile solution method of Mn2+‐doping, (HIS)PbCl4:2%Mn has an orange‐red emission with a high photoluminescence quantum yield (PLQY) of 80.2%. Emphatically, (HIS)PbCl4:2%Mn crystals exhibit compelling scintillation performances, including a high light yield of 77 500 photons MeV−1 (≈3.1 times of commercial Lu3Al5O12:Ce3+) and low detection limits of 59.3 nGyair s−1, along with excellent irradiation and environmental stability. Besides, both centimeter‐size single crystals and flexible scintillators fabricated with poly(dimethylsiloxane) hold a promise for practical X‐ray imaging. The high resolution using an optimized flexible scintillator screen reaches up to 20.3 lp mm−1, far beyond the most of known scintillators. The results suggest that (HIS)PbCl4:2%Mn has great commercialization potential in the field of X‐ray imaging. [ABSTRACT FROM AUTHOR]

Advancements in technology has accelerated the evolution of bone age assessment (BAA) methodologies, one of which is deep learning algorithms, which overcome the drawbacks of conventional approaches. In spite of excellent effectiveness of deep neural networks in detection of the correct class for bone age, they have a significant degree of complexity due to the numerous parameters they employ for each region of interest (ROI). In this paper, we propose a BAA method using a hybrid knowledge distillation (KD) paradigm in order to conquer this difficulty by mapping different ROIs into a single ROI. In this regard, the student receives knowledge from a teacher network that has been pre-trained on six ROIs including bones of five fingers and the wrist, transfers the knowledge of its final response layer and internal layers to the student. Then, six student models each of which is constructed based on just one of these ROIs, while receiving the information of the teacher model. Empirical results on digital hand atlas report that our student model trained on one ROI obtains 95% accuracy on 19 classes of bone age. [ABSTRACT FROM AUTHOR]

The limited availability of medical images is a major limitation when using deep learning, which requires large amounts of data to improve performance. To address this problem, transfer learning has become the de facto standard, using convolutional neural networks (CNNs) previously trained on natural images, and fine-tuned on medical images. Recently, vision transformers (ViT), which require large annotated medical images, have been studied from various perspectives. In this study, we investigated an effective pre-training method for binary classification of COVID-19 using chest radiography (CXR) images. Our results showed that pre-training on natural images outperformed CXR images using the fine-tuning method. Pre-training on natural images was also able to capture more global features. Furthermore, this trend increased as the number of pre-trained on natural images increased. In summary, these results suggest that the fine-tuning method with a large number of natural images as pre-training had the best discrimination performance. [ABSTRACT FROM AUTHOR]

3D visualisation and modelling of anatomical structures of the human body play a significant role in diagnosis, computer-aided surgery, surgical planning, and patient follow-up. However, 2D X-ray images are often used in clinical routine. We propose and validate a method for reconstructing 3D shapes from 2D X-ray scans. This method comprises automatic segmentation and labelling, automated construction of 3D statistical shape models (SSM), and automatic fitting of the SSM to standard 2D X-ray images. This workflow is applied to finger bone shape reconstruction and validated for each finger bone using a set of five synthetic reference configurations and 34 CT/X-ray data pairs. We reached submillimetre accuracy for 91.59% of the synthetic data, while 79.65% of the clinical cases show surface errors below 2 mm. Thus, applying the proposed method can add valuable 3D information where 3D imaging is not indicated. Moreover, 3D imaging can be avoided if the 2D-3D reconstruction accuracy is sufficient. [ABSTRACT FROM AUTHOR]

In this work, the segmented dental X-ray images obtained by dentists have been classified into ideal/minimally compromised edentulous area (no clinical treatment needed immediately), partially/moderately compromised edentulous area (require bridges or cast partial denture) and substantially compromised edentulous area (require complete denture prosthesis). A total of 116 image dental X-ray dataset is used, of which 70% of the image dataset is used for training the convolutional neural network (CNN) while 30% is used sfor testing and validation. Three pretrained deep neural networks (DNNs; SqueezeNet, ResNet-50 and EfficientNet-b0) have been implemented using Deep Network Designer module of Matlab 2022. Each of these CNNs were trained, tested and optimised for the best possible accuracy and validation of dental images, which require an appropriate clinical treatment. The highest classification accuracy of 98% was obtained for EfficientNet-b0. This novel research enables the implementation of DNN parameters for automated identification and labelling of edentulous area, which would require clinical treatment. Also, the performance metrics, accuracy, recall, precision and F1 score have been calculated for the best DNN using confusion matrix. [ABSTRACT FROM AUTHOR]

Nowadays, deep neural networks (DNNs) are helpful tools for mammogram classification in breast cancer screening. But, in Vietnam, there is a relatively small number of mammograms for training DNNs. Therefore, this study aims to apply transfer learning techniques to improve the performance of DNN models. In the first step, 10,418 breast cancer images from the Digital Database for Screening Mammography were used for training the CNN model ResNet 34. In the second step, we fine-tune this model on the Hanoi Medical University (HMU) database with 6,248 Vietnamese mammograms. The optimal model of ResNet 34 among these models achieves a macAUC of 0.766 in classifying breast cancer X-ray images into three Breast Imaging-Reporting and Data System (BI-RADS) categories, BI-RADS 045 ('incomplete and malignance'), BI-RADS 1 ('normal'), and BI-RADS 23 ('benign'), when tested on the test dataset. This result is higher than the result of the ResNet 50 model trained only on the X-ray dataset of 7,912 breast cancer images of the HMU dataset, which achieves a macAUC of 0.754. A comparison of the performance of the proposed model of ResNet 34 applying transfer learning with other works shows that our model's evaluation results are higher than those of the compared models. [ABSTRACT FROM AUTHOR]

Scintillators that exhibit excellent scintillation performance coupled with superior radiation resistance are anticipated to cater the growing demand of X-ray detection across various fields including medicine and scientific research. Oxyfluoride glasses, which combine the benefits of both fluorine and oxide glasses, show significant potential as scintillator candidates. Here, highly transparent Tb3+ doped oxyfluoride glasses with splendid radioluminescence were successfully synthesized by melt quenching method with the aid of a two-step component tuning strategy. The luminescence excited by X-ray and ultraviolet light are significantly enhanced by boosting the effective atomic number of glass components and optimizing the B 2 O 3 /Al 2 O 3 ratio. The integrated X-ray excited luminescence intensity of representative specimen reaches 219% of that of BGO. Exceptional anti-irradiation recoverability and thermal stability of 93% @ 573 K are conferred in the resultant glass. Moreover, X-ray imaging with a spatial resolution of up to 20 lp mm−1 was triumphantly achieved. These properties testify that the prepared oxyfluoride glass owns an enormous application foreground in X-ray imaging. [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]

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]

To answer NASA's call for a sensitive X-ray observatory in the 2030s, we present the High Energy X-ray Probe (HEX-P) mission concept. HEX-P is designed to provide the required capabilities to explore current scientific questions and make new discoveries with a broadband X-ray observatory that simultaneously measures sources from 0.2 to 80 keV. HEX-P's main scientific goals include: 1) understand the growth of supermassive black holes and how they drive galaxy evolution; 2) explore the lower mass populations of white dwarfs, neutron stars, and stellar-mass black holes in the nearby universe; 3) explain the physics of the mysterious corona, the luminous plasma close to the central engine of accreting compact objects that dominates cosmic X-ray emission; and 4) find the sources of the highest energy particles in the Galaxy. These goals motivate a sensitive, broadband X-ray observatory with imaging, spectroscopic, and timing capabilities, ensuring a versatile platform to serve a broad General Observer (GO) and Guest Investigator (GI) community. In this paper, we present an overview of these mission goals, which have been extensively discussed in a collection of more than a dozen papers that are part of this Research Topic volume. The proposed investigations will address key questions in all three science themes highlighted by Astro2020, including their associated priority areas. HEX-P will extend the capabilities of the most sensitive low- and high-energy X-ray satellites currently in orbit and will complement existing and planned high-energy, time-domain, and multi-messenger facilities in the next decade. [ABSTRACT FROM AUTHOR]

AbstractThe conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty issues. This work proposes the automatic detection and classification of COVID-19 by analysing the chest X-ray images (CXR) with the deep convolutional neural network (DCNN) models through a fine-tuning and pre-training approach. CXR images pertaining to four health scenarios, namely, healthy, COVID-19, bacterial pneumonia and viral pneumonia, are considered and subjected to data augmentation. Two types of input datasets are prepared; in which dataset I contains the original image dataset categorised under four classes, whereas the original CXR images are subjected to image pre-processing via Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm and Blackhat Morphological Operation (BMO) for devising the input dataset II. Both datasets are supplied as input to various DCNN models such as DenseNet, MobileNet, ResNet, VGG16, and Xception for achieving multi-class classification. It is observed that the classification accuracies are improved, and the classification errors are reduced with the image pre-processing. Overall, the VGG16 model resulted in better classification accuracies and reduced classification errors while accomplishing multi-class classification. Thus, the proposed work would assist the clinical diagnosis, and reduce the workload of the front-line healthcare workforce and medical professionals. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) scanners are essential for modern imaging but require around 600 projections from various angles. We present x-ray-induced acoustic computed tomography (XACT), a method that uses radiation-induced acoustic waves for three-dimensional (3D) x-ray imaging. These spherical acoustic waves travel through tissue at 1.5 × 103 meters per second, much slower than x-rays, allowing ultrasound detectors to capture them and generate 3D images without mechanical scanning. We validate this theory by performing 3D numerical reconstructions of a human breast from a single x-ray projection and experimentally determining 3D structures of objects at different depths. Achieving resolutions of 0.4 millimeters in the XZ plane and 3.5 millimeters in the XY plane at a depth of 16 millimeters, XACT demonstrates the ability to produce 3D images from one x-ray projection, reducing radiation exposure and enabling gantry-free imaging. XACT shows great promise for biomedical and nondestructive testing applications, potentially replacing conventional CT. [ABSTRACT FROM AUTHOR]

Nanostructured scintillators, renowned for their exceptional miniaturization and portability, are typically designed with homogeneous dopant ion concentration profiles. While these profiles facilitate consistent optical properties, they may pose challenges in terms of compromising light emission intensity and overall scintillation efficiency. A pressing issue in the field of X‐ray flat‐panel minidetectors is the lack of specific and innovative strategies to significantly enhance radioluminescence capabilities, which has hindered further advancements. This research showcases an efficacious strategy for synthesizing ligand‐exchange‐passivated mixed‐halide double perovskite nanocrystals (NCs) tailored for their remarkable scintillation capabilities. The mixed‐halide composition is fine‐tuned via anion exchange between Bi3+ and Tb3+‐doped Cs2AgInCl6 NCs and potassium bromide (KBr). Additionally, the initial oleic acid ligands are substituted with 1‐dodecanethiol (1‐DT), effectively compensating for inherent halogen vacancies and mitigating halide ion migration. The underlying passivation mechanism is elucidated through a comprehensive approach that combined spectroscopic experiments and theoretical calculations. Consequently, the fabricated transparent scintillator films, incorporating synthesized mixed‐halide double perovskite NCs, exhibit a high light yield of ≈20 952 photons MeV−1, a sensitive detection limit of 207.5 nGyair s−1, exceptional spatial resolution of 8.1 lp mm−1, and unparalleled stability under prolonged X‐ray irradiation. [ABSTRACT FROM AUTHOR]

0D organic‐inorganic Cu(I)‐based halides have gained significant attention due to their low toxicity, structural adjustability, and moderate fabrication conditions. However, it is still challenging to explore stable and efficient 0D hybrid Cu(I)‐based halides that have phase transition and tunable spectra for multifunctional photoelectric applications. Herein, two 0D copper halides, green‐emissive (MTPP)2CuI3 and yellow‐emissive (MTPP)2Cu4I6(MTPP = Methyltriphenylphosphonium), are successfully synthesized using a slow cooling method. Both compounds exhibit high photoluminescence quantum yield (PLQY) of 81.95 and 99.7%, and remarkable steady‐state light yield of 38 750 and 63 700 photons per MeV, respectively. The scintillation screen of the two compounds based on vacuum‐filtration enables high X‐ray imaging resolution of 17.83 and 18.49 lp mm−1, showing great potential in practical X‐ray imaging applications. Moreover, a reversible and fast phase transformation between them occurs when stimulated by ethanol or MTPP solutions, without requiring additional thermal treatment, which endows them with a high level of anti‐counterfeiting under room temperature (RT). It is worth noting that they display remarkable resistance to water, maintaining its phase purity even after being immersed in water for 30 days. This study introduces a new approach to investigate the 0D Cu‐based halides that exhibit excellent scintillation performance, stability, and efficient photoluminescence tunability for multiple applications. [ABSTRACT FROM AUTHOR]

X-rays can provide images when an object is visibly obstructed, allowing for motion measurements via x-ray digital image correlation (DIC). However, x-ray images are path-integrated and contain data for all objects between the source and detector. If multiple objects are present in the x-ray path, conventional DIC algorithms may fail to correlate the x-ray images. A new DIC algorithm called path-integrated (PI)-DIC addresses this issue by reformulating the matching criterion for DIC to account for multiple, independently-moving objects. PI-DIC requires a set of reference x-ray images of each independent object. However, due to experimental constraints, such reference images might not be obtainable from the experiment. This work focuses on the reliability of synthetically-generated reference images, in such cases. A simplified exemplar is used for demonstration purposes, consisting of two aluminum plates with tantalum x-ray DIC patterns undergoing independent rigid translations. Synthetic reference images based on the "as-designed" DIC patterns were generated. However, PI-DIC with the synthetic images suffered some biases due to manufacturing defects of the patterns. A systematic study of seven identified defect types found that an incorrect feature diameter was the most influential defect. Synthetic images were re-generated with the corrected feature diameter, and PI-DIC errors were improved by a factor of 3-4. Final biases ranged from 0.00-0.04 px, and standard uncertainties ranged from 0.06-0.11 px. In conclusion, PI-DIC accurately measured the independent displacement of two plates from a single series of path-integrated x-ray images using synthetically-generated reference images, and the methods and conclusions derived here can be extended to more generalized cases involving stereo PI-DIC for arbitrary specimen geometry and motion. This work thus extends the application space of x-ray imaging for full-field DIC measurements of multiple surfaces or objects in extreme environments where optical DIC is not possible. [ABSTRACT FROM AUTHOR]

Fusion-based welding and additive manufacturing are two key pillars of manufacturing. Rapidly evolving melt pools are associated with both of these processing approaches. Understanding and controlling the evolution of the melt pools are critical for optimization of such processes. Flow and interface oscillation during those processes are closely linked to the final fusion zone and microstructure formation. Synchrotron X-ray radiography enables observation of transient melt pools in additive manufacturing and welding processes in real time. However, analysis of the large amount of data generated in such experiments are cumbersome. Thus, we have examined the potential to analyse fast time-resolved X-ray image sequences of melt pools with image-based convolutional neural networks. The results demonstrate successful recognition of changes in the fluctuations of melt-pool interfaces associated with rapid-flow evolution. [ABSTRACT FROM AUTHOR]

Modern security faces challenges in detecting unauthorized and potentially harmful items in luggage, despite X-ray baggage scanning frameworks and research on efficiently screening highly disguised items. In response to this gap, a groundbreaking Modified Encoder-Decoder-based model has been introduced. This innovative model takes X-ray scan images as input and generates distinct feature representations for both suspicious and non-suspicious baggage materials. A key focus of the model is to address the denoising challenge inherent in X-ray images which reduces the models efficiency. This is achieved through the implementation of a Poisson Noise Reduction method during the preprocessing stage. Following preprocessing, the model effectively segments the non-threat image, identifying potential threats from the denoised input. The model showcases superior performance, as evidenced by high Peak Signal-to-Noise Ratio (PSNR) and low Mean Squared Error (MSE) values, outperforming existing filtering techniques. Rigorous testing on publicly available SIXray and GDXray datasets validates the effectiveness of the proposed methodology. Performance metrics for the SIXray dataset, including mAP, IoU, and DC values of 97.32%, 73.14%, and 85.12%, respectively, underscore the model's efficacy. Notably, the framework attains an impressive accuracy of 99.17% on the SIXray dataset, affirming its robustness in addressing contemporary security challenges. [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]

X-ray Talbot and Talbot–Lau interferometers consisting of transmission gratings are widely used for X-ray phase imaging, which depicts soft materials. This study exploits the use of a pair of concave and convex parabolic gratings instead of a conventional rectangular phase grating to enhance the phase signal optically. To gain insight into the optimal design, signal enhancement is evaluated by directly measuring the self-image formed downstream of the pair. An increase in the differential phase signals is demonstrated as a function of the distance between the pair, and prospects for deploying this concept into a practical phase imaging technique are discussed. [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]

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]

Coronavirus 2019 causes COVID-19, a worldwide epidemic. It endangers millions globally. Early illness detection improves recovery and control. X-ray image processing is used to categorise and identify COVID-19 in the present study. Preprocessing, feature extraction using local binary pattern (LBP) and edge orient histogram (EOH), and classification utilising K-nearest neighbour (KNN), Navie Bayes, support vector machine (SVM), and transfer learning convolution neural networks (CNNs) are some of the stages that are implemented in the process. Other phases in the process include preprocessing, feature extraction, and preprocessing. LBP+KNN, EOH+KNN, LBP+SVM, EOH +SVM, CNN+LBP, and CNN+EOH are the outputs derived from the combinations of feature extraction operators and classifiers. Other possible outcomes are CNN+EOH and CNN+LBP. A total of 4,000 pictures were used as the basis for conducting an analysis of the performance of six different models. In order to train the models, 10-fold cross-validation was used, and their accuracy was measured accordingly. The evaluation results indicate a high level of accuracy in diagnosis, ranging from 90.2% to 97.56%. The CNN+LBP and CNN+EOH models have demonstrated superior performance compared to other models, achieving average accuracies ranging from 96.66% and 98.54%.. [ABSTRACT FROM AUTHOR]

Lung diseases often result in severe damage to the respiratory tract, and lead to a high risk of mortality within a short period of time. DL models based on ViT are considered to have promising advantages over CNN architectures in terms of computational efficiency, and accuracy when trained on large ImageNet datasets. In this study, we present a new DL approach based on the combination of CNN with ViT to improve the efficiency of pneumonia diagnosis using medical images. In the first stage, raw images are passed through a local filter to capture local relations on the inputs. The local filter block includes two convolutional layers with kernel 3 × 3. This local filtering method aims to enhance rich features before being fed into the patching layer of the ViT block. The proposed method is experimented on the benchmark chest X-ray dataset. The proposed method is evaluated and compared to some well-known models, which include ViT, VGG19, Resnet50, Densnet201. Experimental results demonstrated that the proposed approach based on CNN and ViT reaches higher efficiency with about 1% accuracy to the standard ViT model, and about 2% higher with VGG19, Resnet50, Densnet201 and smaller in model architecture. [ABSTRACT FROM AUTHOR]

In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a redesigned feature extraction (FE) module and multiscale feature (MF) module and validated using publicly available COVID-19 datasets. Experiments were conducted on multiple updated versions of the COVID-19 Radiography Database, a publicly accessible dataset on Kaggle. The database contained images categorized into three classes: COVID-19 coronavirus, viral or bacterial pneumonia, and normal. The results revealed that the proposed method achieved a training accuracy of 99.85% and a validation accuracy of 96.28% when detecting the three classes. In the test set, the optimal results were 96.03% accuracy for COVID-19, 97.10% accuracy for viral or bacterial pneumonia, and 97.86% accuracy for normal individuals. By reducing the computational requirements and improving the speed of the model, the proposed method can achieve real-time, low-error performance to help medical professionals with rapid diagnosis of COVID-19. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT platforms, challenges remain in effectively inspecting them for actionable insights, either individually or in a multidisciplinary, geographically distributed team setting. Quality assurance, manufacturing departments, pilot labs, and plant operations must collaborate closely to reliably produce parts at scale. This is particularly crucial in AM where complex structures require a collaborative and multidisciplinary approach. Additionally, the large-scale data originating from different modalities and their inherent 3D nature pose significant hurdles for traditional 2D desktop-based inspection methods. To address these challenges and increase the value proposition of DTs, we introduce a novel virtual reality (VR) framework to facilitate collaborative and real-time inspection of DTs in AM. This framework includes advanced features for intuitive alignment and visualization of multimodal data, visual occlusion management, streaming large-scale volumetric data, and collaborative tools, substantially improving the inspection of AM components and processes to fully exploit the potential of DTs in AM. [ABSTRACT FROM AUTHOR]

Phase contrast x-ray imaging (PCXI) provides high-contrast images of weakly-attenuating structures like the lungs. PCXI, when paired with 4D X-ray Velocimetry (XV), can measure regional lung function and non-invasively assess the efficacy of emerging therapeutics. Bacteriophage therapy is an emerging antimicrobial treatment option for lung diseases such as cystic fibrosis (CF), particularly with increasing rates of multi-drug-resistant infections. Current efficacy assessment in animal models is highly invasive, typically requiring histological assessment. We aim to use XV techniques as non-invasive alternatives to demonstrate efficacy of bacteriophage therapy for treating Pseudomonas aeruginosa CF lung infections, measuring functional changes post-treatment. Time-resolved in vivo PCXI-CT scans of control, Pseudomonas-infected, and phage-treated mouse lungs were taken at the Australian Synchrotron Imaging and Medical Beamline. Using XV we measured local lung expansion and ventilation throughout the breath cycle, analysing the skew of the lung expansion distribution. CT images allowed visualisation of the projected air volume in the lungs, assessing structural lung damage. XV analysis demonstrated changes in lung expansion between infection and control groups, however there were no statistically significant differences between treated and placebo groups. In some cases where structural changes were not evident in the CT scans, XV successfully detected changes in lung function. [ABSTRACT FROM AUTHOR]

We propose a multi-frame blind deconvolution method using an in-plane rotating sample optimized for X-ray microscopy, where the application of existing deconvolution methods is technically difficult. Untrained neural networks are employed as the reconstruction algorithm to enable robust reconstruction against stage motion errors caused by the in-plane rotation of samples. From demonstration experiments using full-field X-ray microscopy with advanced Kirkpatrick–Baez mirror optics at SPring-8, a spatial resolution of 34 nm (half period) was successfully achieved by removing the wavefront aberration and improving the apparent numerical aperture. This method can contribute to the cost-effective improvement of X-ray microscopes with imperfect lenses as well as the reconstruction of the phase information of samples and lenses. [ABSTRACT FROM AUTHOR]

The thickness of X-ray detectors needs to reach hundreds of micrometers to absorb X-ray, and therefore, high voltages over tens or hundreds of volts should be applied to extract X-ray-generated carriers. Here, we propose a bulk Schottky junction for X-ray detection using interpenetrated macroporous-carbon electrodes and metal-halide perovskite networks. The X-ray-generated holes are extracted by the macroporous-carbon electrodes under the built-in electric field, while the electrons in the perovskite phase result in a high gain effect. A high sensitivity of 1.42×105 μC Gyair−1 cm−2 and a low detection limit of 48 nGyair s−1 at a low voltage of −1 V are achieved. We fabricated a dry battery-powered portable X-ray alarm prototype. The pixel detector shows a decent stability under X-ray exposure, and a spatial resolution of up to 5.0 lp mm−1, and the detector arrays also exhibit remarkable uniformity, demonstrating its potential application in X-ray imaging. Zhang et al. report a bulky Schottky junction constructed by interpenetrated macroporous-carbon electrode for hole extraction and perovskite network for electron trapping under X-ray irradiation. The efficient extraction of carriers enables a dry battery-powered portable X-ray radiation alarm. [ABSTRACT FROM AUTHOR]

Perovskite nanocrystals (PNCs) show excellent optoelectronic performance and controlled synthesis of PNCs is arising as research hot spot recently. Transparent all‐inorganic glass matrix has been proved perfect protector of PNCs from aggregation. However, the precise precipitation of PNCs in glass with high photoluminescence quantum yield (PLQY) through a facile routing is still challenge. Here, site‐specifically crystallization of PNCs are rationally designed in all‐inorganic transparent solid matrix through surface electric dipole moment engineering, achieving exceptional PLQY (89.9%). Modifying the orientation of the surface electric dipole moment reduces the water‐induced nucleation barrier and promotes the crystallization of PNCs at the selected sites. In addition, the inherent ionic structure and low formation energy of CsPbBr3 PNCs allow the luminescent structure to be erased by annealing and then recovered by water‐inducing. Combining the intense luminescence, high stability and completely reversable crystallization, the applications of PNCs‐glass in information encryption and reusable X‐ray dosimeter and imaging are demonstrated. The PNCs‐glass scintillation screen achieves the X‐ray imaging resolution of 17.5 lp mm−1, which is the highest among all the PNCs based scintillation screen. [ABSTRACT FROM AUTHOR]

The instability of halide perovskite nanocrystals (PNCs) and related composite polymer films posed considerable challenges for application in flexible optoelectronic devices. Herein, perovskite nanocrystal‐polymethyltrifluoropropylsiloxane (PNCs‐PMFS) composites are developed that exhibit outstanding optical stability and irradiation resistance through a mutually reinforcing strategy. The photoluminescence (PL) intensity of PNCs‐PMFS remained stable after four heating cycles, whereas perovskite nanocrystal‐polydimethylsiloxane (PNCs‐PDMS) composites exhibited a 31% decrease in PL intensity. Moreover, PNCs‐PMFS demonstrated superior luminescence stability under UV and X‐ray irradiation due to strong ion‐dipole interactions between PNCs and trifluoromethyl (CF3) dipoles. Under γ‐ray irradiation (300 kGy), PNCs‐PMFS retained 73% (2.86 MPa) of the initial mechanical strength, while PMFS without PNCs retained only 51%. This enhancement is attributed to the effective reduction of free radical concentration in the system by PNCs, as confirmed by electron spin resonance (ESR) and curing curve. Density‐functional theory (DFT) calculations further indicated that PNCs adsorbed free radicals, thereby facilitating interfacial charge transfer and forming a stable resonance structure. These advancements enabled PNCs‐PMFS to serve as scintillation screens for X‐ray detection and imaging, achieving a spatial resolution of 19.0 lp mm−1 and a detection limit of 3.78 µGy s−1, offering novel insights for designing of X‐ray detectors in high‐energy radiation environments. [ABSTRACT FROM AUTHOR]

We present deep, wideband multifrequency radio observations (144 MHz−8 GHz) of the remarkable galaxy group NGC 741, which yield crucial insights into the interaction between the infalling head-tail radio galaxy (NGC 742) and the main group. Our new data provide an unprecedentedly detailed view of the NGC 741-742 system, including the shock cone, disrupted jets from NGC 742, the long (∼255 kpc) braided southern radio tail, and the eastern lobe-like structure (∼100 kpc), and reveal, for the first time, complex radio filaments throughout the tail and lobe, and a likely vortex ring behind the shock cone. The cone traces the bow shock caused by the supersonic ( M ∼ 2 ) interaction between the head-tail radio galaxy NGC 742 and the intragroup medium (IGrM), while the ring may have been formed by the interaction between the NGC 742 shock and a previously existing lobe associated with NGC 741. This interaction plausibly compressed and reaccelerated the radio plasma. We estimate that shock-heating by NGC 742 has likely injected ∼2–5 × 1057 erg of thermal energy into the central 10 kpc cooling region of the IGrM, potentially affecting the cooling and feedback cycle of NGC 741. A comparison with Chandra X-ray images shows that some of the previously detected thermal filaments align with radio edges, suggesting compression of the IGrM as the relativistic plasma of the NGC 742 tail interacts with the surrounding medium. Our results highlight that multifrequency observations are key to disentangling the complex, intertwined origins of the variety of radio features seen in the galaxy group NGC 741, and the need for simulations to reproduce all the detected features. [ABSTRACT FROM AUTHOR]

High transparency Ce3+-doped dense gadolinium aluminum borosilicate (GS x) glass scintillator was synthesized in reducing atmosphere by melt-quenching process. The transmittance of the glasses exceeds 80%, and the light attenuation length is between 5-20 cm within the range of 400–600 nm. GS x glass shows an photoluminescence (PL) in range of 350–550 nm, with the strongest emission peak around 420 nm. The PL decay time of GS x glasses is around 37 ns, with a maximum PL quantum yield of 44.05%. The integrated X-ray excited luminescence (XEL) intensity of GS S glass is 52.5% compared with BGO crystal. For X-ray imaging, GS L glass based imaging system shows a high spatial resolution of 9 lp/mm, which is comparable to CsI:Tl X-ray detectors. Under γ-ray, the highest light yield of GS S glass is 1235 ph/MeV with an energy resolution of 24.0% at 662 keV, close to that of BSO crystal. The scintillation decay time of GS x glasses is consisted of fast (∼100 ns) and slow (∼560 ns) components. In thermally stimulated luminescence (TSL), GS S glass exhibits a strong TSL peak at approximately 440 K, with low intensity shoulders at about 520 K, implying different types of defects and traps. Therefore, GS x glass has promising prospects for X-ray imaging and high-energy physics applications. [ABSTRACT FROM AUTHOR]

Radiological findings play an essential and complementary role in diagnosing Covid-19, assessing its severity, and managing its patients. Artificial intelligence technology based on medical imaging, which has made exciting developments by being applied in many areas, has become an area of interest for the rapid and accurate detection of the disease in the fight against the Covid-19 pandemic. The main difficulty is the inability to obtain a large dataset size with quality and standard images that neural networks need to perform well. Aiming at this problem, this study proposes a Siamese neural network-based deep learning framework for accurate diagnostics of Covid-19 using chest X-ray (CXR) images. The pre-trained VGG16 architecture, based on the transfer learning approach, forms the backbone of the Siamese neural network. The outputs of the backbones are joined together by a merging layer, and then the output passes through a fully connected layer. Based on this structure, category-aware Siamese-based models are produced for each class. The predictions from the models are combined using a voting mechanism to reduce the possibility of misclassification and to make better decisions. The framework was evaluated using a publicly available dataset for the 4-class classification task for Covid-19 pneumonia, lung opacity, normal, and non-Covid-19 viral pneumonia images. The findings reveal the high discrimination ability of the framework, trained using only 10 images per class in less training time, achieving an average test accuracy of 92%. Our framework, which learns a single Siamese-based pairwise model for each class, effectively captures class-specific features. Additionally, it has the potential to deal with data scarcity and long training time problems in multi-class classification tasks. [ABSTRACT FROM AUTHOR]

Purpose: The focus of this article is to describe the configuration, testing, and commissioning of a novel gantry-less synchrotron-based proton therapy (PT) facility. Materials and methods: The described PT system delivers protons with a water equivalent range between 4 and 38 cm in 1800 energy layers. The fixed beam delivery permits a maximum field size of 28 × 30 cm2. The patient positioning and imaging system includes a six-degree-of-freedom robotic arm, a convertible patient chair, a vertical 4DCT, and an orthogonal 2D X-ray imaging system. Results: The spot positioning reproducibility was consistent within ±1 mm. The width (σ) of the beam profile at the isocenter was energy dependent and ranged from 2.8 mm to 7.7 mm. Absolute dose reproducibility was measured and deviations were found to be <0.62% for all possible beam scenarios. The built-in dose monitoring system was successfully tested for its ability to generate interlocks under specific conditions (beam spot deviation ≥2 mm, individual spot dose ≥10% or ≥0.25 Gy, spot energy deviation ≥0.5 MeV). The robot positioning exhibited a consistent reproducibility within ±1 mm. All tested scenarios achieved laser-free initial 3D/3D image-guided positioning within ±5 mm. Subsequent 2D/3D positioning showed an accuracy of ±1 mm. A single 2D/3D image registration event corrected positions in all cases. Results of gamma analysis (3%, 3 mm) demonstrated pass rates greater than 95% for head and neck, thorax, abdomen treatment plans. Conclusions: We report on the performance of a novel single-room gantry-less PT system comprised of a compact synchrotron and an adjustable (from nearly horizontal to almost vertical) patient positioning system. The commissioning results show high accuracy and reproducibility of the main proton beam parameters and the patient positioning system. The new PT facility started patient treatments in March 2023, which were the first in Israel and the Middle Eastern region. [ABSTRACT FROM AUTHOR]

Glass, with its unique amorphous properties, offers low thermal conductivity, high catalytic activity, insensitivity to interfacial lattice mismatch, and the absence of grain boundaries. Melt‐quenched organic–inorganic hybrid glass has recently gained significant attention as an emerging material because of its excellent processability and formability. Here, an SbCl3(C25H46ClN)x halide with a low melting point (90 °C) and significant formability is reported. Both the crystalline and glass states of SbCl3(C25H46ClN)x have double broadband emission, and the glass state exhibits negative thermal quenching, which is rare in metal halides. Interestingly, the luminescence properties of SbCl3(C25H46ClN)x glass with different x values differ. This feature is utilized to design multimodal anti‐counterfeiting and information encryption applications. Additionally, The inherent melt processing capability of SbCl3(C25H46ClN)x allows it to be shaped into various forms suitable for practical applications. SbCl3(C25H46ClN)x scintillator screens (diameter 2.2 cm) are successfully prepared by low‐temperature melting, achieving an X‐ray imaging resolution of 18 line pairs per millimeter (18 lp mm−1). This study demonstrates the potential of melt‐processed organic–inorganic hybrid glass SbCl3(C25H46ClN)x in anti‐counterfeiting, information encryption, and X‐ray detection. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging remains the gold standard for diagnosing osteoporotic vertebral compression fractures (OVCF), but the use of X-ray imaging, particularly anteroposterior (AP) and lateral views, is prevalent due to its accessibility and cost-effectiveness. We aim to assess whether the performance of AP images-based deep learning is comparable compared to those using lateral images. This retrospective study analyzed X-ray images from two tertiary teaching hospitals, involving 1,507 patients for the training and internal test, and 104 patients for the external test. The EfficientNet-B5-based algorithms were employed to classify OVCF and non-OVCF group. The model was trained with a 1:1 balanced dataset and validated through 5-fold cross validation. Performance outcomes were compared with the area under receiver operating characteristic (AUROC) curve. Out of a total of 1,507 patients, 799 were included in the training dataset and 708 were included in the internal test dataset. The training and internal test datasets were matched 1:1 as OVCF and non-OVCF patients. The DL model showed comparable classifying performance with internal test data (N = 708, AUROC for AP, 0.915; AUROC for lateral, 0.953) and external test data (N = 104, AUROC for AP, 0.982; AUROC for lateral, 0979), respectively. The other performances including F1 score and accuracy were also comparable. Especially, The AUROC of AP and lateral x-ray image-based DL was not significantly different (p for DeLong test = 0.604). The EfficientNet-B5 algorithms using AP X-ray images shows comparable efficacy for classifying OVCF and non-OVCF compared to lateral images. [ABSTRACT FROM AUTHOR]

X-ray imaging with a large field of view (FOV) and high resolution is extremely important for Rayleigh–Taylor instability measurement with a small amplitude and high spatial frequency in laser inertial confinement fusion. We developed an advanced Kirkpatrick–Baez (AKB) microscope based on the quadratic-aberration theory to realize a large FOV and high resolution. This microscope was assembled and tested in a laboratory, and it was then successfully applied for imaging the hydrodynamic instability of a perturbation target in implosion experiments at the Shenguang-III prototype laser facility. Imaging results demonstrate that the AKB microscope can achieve an optimal resolution of ~ 0.53 μm and ~ 0.40 μm and a spatial resolution of < 1.5 μm within a 300-µm FOV and < 4.5 μm in a 1-mm FOV. [ABSTRACT FROM AUTHOR]

Lead halide perovskites have emerged as attractive X‐ray detector materials, owing to properties such as strong X‐ray stopping power, excellent carrier transport, and high sensitivity. Additionally, they can be easily prepared by using solution‐based synthesis approaches. However, traditional 3D (three‐dimensional) perovskites X‐ray detectors have shown limited application due to high dark currents generated under bias voltage as a result of strong ion migration. In this work, an X‐ray detector with a vertical structure device is demonstrated using 2D (two‐dimensional) histamine lead halide perovskite single crystal HAPbI4 (HPI, HA = histamine). Due to the dielectric screening effect of diamine and the vertical structure of the HPI device, the fabricated detector shows a sensitivity of 7737 µC Gyair−1 cm−2 under a bias voltage of 30 V. Furthermore, the detector shows a sensitivity of 293 µC Gyair−1 cm−2 and detection limit of 51.38 nGyair s−1 without bias voltage, wherein the dark current is almost completely suppressed. All of these properties indicate the X‐ray detection device is a promising candidate for next‐generation optoelectronic applications. [ABSTRACT FROM AUTHOR]

Coronavirus (also known as COVID-19) is an extremely contagious disease spreading around the globe. An efficient and fast diagnostic method must be designed to identify COVID-19 patients. There are several methods for identification and monitoring of the disease, namely radiological imaging of the patient's chest and polymerase chain reaction (RT-PCR) test. Recent investigations have shown that radiological images are used to observe the effect of COVID on the lungs. Deep Learning is proven effective for image detection and classification in many applications. The majority of existing COVID-19 architectures detect irrelevant features for decision-making. In this paper, a novel network called XcepCovidNet is proposed for feature detection of chest X-rays. It employs transfer learning using hyperparameter-tuning to account for the inadequacies of the training dataset. The proposed model is found superior to pre-trained models such as VGG-19, ResNet-50, DenseNet-201, Xception, and DarkNet-19, in terms of different performance metrics. It is an automated, fast, reliable, and precise COVID-19 detection system for initial screening and diagnosing infected individuals. The obtained results indicate that XcepCovidNet yielded an accuracy of 98.67% and 93.66% for binary and four-class classification, respectively. A two-step verification is performed to validate the proposed model using different models of explainable artificial intelligence, i.e., LIME and occlusion sensitivity. [ABSTRACT FROM AUTHOR]

It is difficult to automatically recognize defects using digital image processing methods in X-ray radiographs of lap joints made from plates of unequal thickness. The continuous change in the wall thickness of the lap joint workpiece causes very different gray levels in an X-ray background image. Furthermore, due to the shape and fixturing of the workpiece, the distribution of the weld seam in the radiograph is not vertical which results in an angle between the weld seam and the vertical direction. This makes automatic defect detection and localization difficult. In this paper, a method of X-ray image correction based on invariant moments is presented to solve the problem. In addition, a novel background removal method based on image processing is introduced to reduce the difficulty of defect recognition caused by variations in grayscale. At the same time, an automatic defect detection method combining image noise suppression, image segmentation, and mathematical morphology is adopted. The results show that the proposed method can effectively recognize the gas pores in an automatic welded lap joint of unequal thickness, making it suitable for automatic detection. [ABSTRACT FROM AUTHOR]

Lower-limb alignment deformities constitute a significant clinical concern, as they can lead to serious complications, including progressive degenerative diseases and disabilities. Rotational deformities may give rise to conditions such as joint arthrosis, patellar instability, and the degeneration of the patellofemoral cartilage. Therefore, a comprehensive evaluation of lower-limb alignment is essential for the effective patient management, preoperative planning, and successful correction of these deformities. The primary assessment method employs full-length standing radiographs in the anteroposterior (AP) projection, which facilitates accurate measurements of the anatomical and mechanical axes of the lower limb, including angles and deviations. The outcomes of this analysis are vital for the meticulous planning of osteotomy and total knee arthroplasty (TKA). In addition, computed tomography (CT) provides a specialized approach for the precise evaluation of femoral and tibial rotation. In this area, there are potential opportunities for the implementation of AI-based automated measurement systems. [ABSTRACT FROM AUTHOR]