Two-Dimensional Perovskite Single Crystals for High-Performance X-ray Imaging and Exploring MeV X-ray Detection

Scintillation semiconductors play increasingly important medical diagnosis and industrial inspection roles.Recently,two-dimensional(2D)perovskites have been shown to be promising materials for medical X-ray imaging,but they are mostly used in low-energy(≤130 keV)regions.Direct detection of MeV X-rays,which ensure thorough penetration of the thick shell walls of containers,trucks,and aircraft,is also highly desired in practical industrial applications.Unfortunately,scintillation semiconductors for high-energy X-ray detection are currently scarce.Here,This paper reports a 2D(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single crystal with outstanding sensitivity and stability toward X-ray radiation that provides an ultra-wide detectable X-ray range of between 8.20 nGy_(air)s^(-1)(50 keV)and 15.24 mGy_(air)s^(-1)(9 MeV).The(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single-crystal detector with a vertical structure is used for high-performance X-ray imaging,delivering a good spatial resolution of 4.3 Ip mm^(-1)in a plane-scan imaging system.Low ionic migration in the 2D perovskite enables the vertical device to be operated with hundreds of keV to MeV X-ray radiation at high bias voltages,leading to a sensitivity of 46.90μC Gy_(air)-1 cm^(-2)(-1.16 Vμm^(-1))with 9 MeV X-ray radiation,demonstrating that 2D perovskites have enormous potential for high-energy industrial applications.

An attention-based cascade R-CNN model for sternum fracture detection in X-ray images

Fracture is one of the most common and unexpected traumas.If not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve injury.Using computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection speed.However,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult fractures.In this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture detection.The authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local variations.The authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art models.The authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

Application of Dual-Energy X-Ray Image Detection of Dangerous Goods Based on YOLOv7

X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.

A fast and adaptive method for automatic weld defect detection in various real-time X-ray imaging systems

A first and effective method is proposed to detect weld deject adaptively in various Dypes of real-time X-ray images obtained in different conditions. After weld extraction and noise reduction, a proper template of median filter is used to estimate the weld background. After the weld background is subtracted from the original image, an adaptite threshold segmentation algorithm is proposed to obtain the binary image, and then the morphological close and open operation, labeling algorithm and fids＇e alarm eliminating algorithm are applied to pracess the binary image to obtain the defect, ct detection result. At last, a fast realization procedure jbr proposed method is developed. The proposed method is tested in real-time X-ray image,s obtairted in different X-ray imaging sutems. Experiment results show that the proposed method is effective to detect low contrast weld dejects with few .false alarms and is adaptive to various types of real-time X-ray imaging systems.

X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic crisis.In the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected patients.Identifying COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain extent.This paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral infection.This approach includes preprocessing,feature extraction,and classification.However,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature descriptors.Furthermore,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is used.Experimental results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic performance.It is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

Spectrometer of hard X-ray imager payload onboard the ASO-S mission

A spaceborne hard X-ray spectrometer, composed of an array of 99 scintillation detectors and associated readout electronics, has been developed for the hard X-ray imager(HXI). The HXI is one of the three payloads onboard the advanced space-based solar observatory(ASO-S), which is scheduled to be launched in early 2022 as the first Chinese solar satellite. LaBr3 scintillators and photomultiplier tubes with a super bialkali cathode are used to achieve an energy resolution better than 20% at 30 keV.Further, a new multi-channel charge-sensitive readout application-specific integrated circuit guarantees high-frequency data acquisition with low power consumption. This paper presents a detailed design of the spectrometer for the engineering model of the HXI and discusses its noise and linearity performance.

Research on Influence of Electromagnetic Interference on X-Ray Digital Detection

X-ray digital imaging technology has found wide application owing to its advantages of real-time, visualization and rapid imaging. In substations the substantial electromagnetic interference has some influence on the live detection by the X-ray digital imaging technology, hindering the promotion of the technology in the detection of electric equipment. Based on a large number of field tests, the author carded out a series of researches on electromagnetic interference protection measures, image de-noising, and image enhancement algorithms.

Detection technology of micro residual core in small inner cavity of complex castings

A new technology for detecting a tiny residual core in the small inner cavity of complex castings is proposed. The residual core is identified by using image recognition technology. Tracer processing and image signal processing are combined to enhance the image contrast. The relationships between the concentration of tracer, the size of the residual core, the wall thickness of the castings and the contrast were obtained. Based on the experimental data, the minimum detectable amount of residual core under different conditions was obtained. The results show that the minimum detectable amount decreases from 4.398 mg to 0.438 mg for the 1.0 mm wall thickness casting when the concentration of tracer increases from 0% to 20%. The signal-to-noise ratio(SNR) of the detection results increases by 27.010 by means of average filtering and linear point operation. The subtraction of image and image background was performed, and then the boundary extraction was carried out to obtain a clear and reliable result. The experimental results show that the non-traced residual core cannot be detected for a blade with a thickness less than 5 mm. The residual core of 1 mm thickness can be barely identified by artificial recognition after tracer processing and image processing, while the residual core of 0.6 mm thickness can be detected clearly using image recognition technology.

Design and Implementation of High-speed X-ray Nondestructive Detector

A high-speed X-ray nondestructive detector is designed in this paper. The principle of X-ray nondestructive detection is analyzed, and a general system scheme of the high-speed X-ray nondestructive detector is proposed. The Virtex-4 series Fxl2 FPGA chip is used to design its hardware circuit, the PowerPC405 embedded system is developed, the high-speed image processing algorithm is applied to compile its processing software, and TCP/IP protocol is employed to compile the correspondence software, to realize high-speed X-ray signal gathering, processing and transmission. The experimental result indicated that the detector can be applied to the long-distance and on-line nondestructive detection of product line with Steel Wire Ropes in correlative industry field, such as mines, ports and wharfs. The running rate of the conveyer belt could achieve 6m/s when the survey width of the detector is 1.6 m.

Automatic Defect Detection in X-Ray Images Using Image Data Fusion

Automatic defect detection in X-ray images is currently a focus of much research at home and abroad. The technology requires computerized image processing, image analysis, and pattern recognition. This paper describes an image processing method for automatic defect detection using image data fusion which synthesizes several methods including edge extraction, wave profile analyses, segmentation with dynamic threshold, and weld district extraction. Test results show that defects that induce an abrupt change over a predefined extent of the image intensity can be segmented regardless of the number, location, shape or size. Thus, the method is more robust and practical than the current methods using only one method.

Lead-free organic-inorganic hybrid scintillators for X-ray detection

Scintillators,which can convert high-energy particles(X-rays)into detectable lowenergy ultraviolet-visible-near-infrared photons,are essential components of X-ray detectors and show extensive practical applications in nondestructive detection and medical imaging.Traditionally,inorganic scintillators represented by CsI:Tl have achieved definite progress.However,the harsh preparation conditions,high production cost,and poor mechanical properties impede their potential development in the high-end X-ray imaging field.Organic-inorganic hybrid metal complexes could be excellent alternatives,by virtue of their structural and spectral tunability,good solution processability,and excellent photophysical properties.This review mainly focuses on eco-friendly lead-free metal(Mn^(2+),Cu^(+),Sb^(3+),Sn^(2+),Ge^(2+),Ln^(3+),etc.)complex scintillators.The luminescence mechanisms are introduced and the scintillation performance,such as light yield,limit of detection,imaging resolution,etc.,is highlighted.Moreover,the current challenges and perspectives in this emerging field are described.It is hoped to provide some theoretical guidance for the continuous development of the new scintillator systems.

Illuminations for constructions of scintillating lanthanide–organic complexes in sensitive X-ray detection and high-resolution radiative imaging

X-ray detection and imaging via scintillators has been utilized in missions worldwide within areas of scientific research,medical industry,military defense and homeland security.Commercial scintillators are costly with high energy consumption through the sintering.It is of great significance to seek alternative scintillating materials for sensitive X-ray detection in the next-generation.Herein,eight structure-defined Ln(Ⅲ)-based metal-organic frameworks(Ln-MOFs)were prepared,2D[Ln_(2)(1,4-ndc)_(3)(DMF)_(4)]_(n)·nH_(2)O(Ln=Sm 1,Eu 2,Dy 3,Tb 4)and 3D[Ln_(4)(2,6-ndc)_(6)(μ_(2)H_(2)O)2(H_(2)O)_(4)]n·2nH_(2)O(Ln=Sm 5,Eu 6,Dy 7,Tb 8),where 1,4-H_(2)ndc=1,4-naphthalene dicarboxylate acid,2,6-H_(2)ndc=2,6-napthalene dicarboxylate acid,DMF=N,N-dimethylformamide.Merely compounds 2 and 6 show remarkable X-ray scintillation performance via the characteristic red emissions of Eu(Ⅲ)ions,in which the absorbed energy from the triplet states of the organic moieties can be transferred more efficiently to the resonance emission levels of Eu(Ⅲ)ions than other lanthanide(Ⅲ)ions.The X-ray dosage rate detection limits of 2 and 6 are superior to the standard for the medical X-ray diagnosis dosage rate.As proofs-of-concepts,matrix-mixed membranes fabricated with 2 and 6 have achieved remarkable X-ray imaging with high resolution for practical object shooting.

Metal–organic framework wafer enabled fast response radiation detection with ultra-low dark current

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

Highly sensitive diamond X-ray detector array for high-temperature applications

Diamond is a highly suitable material for X-ray detectors that can function effectively in harsh environments due to its unique properties such as ultrawide bandgap,high radiation resistance,excellent carrier mobility as well as remarkable chemical and thermal stability.However,the sensitivity of diamond X-ray detectors needs further improvement due to the relatively low X-ray absorption efficiency of diamond,and the exploration of singlecrystal diamond array imaging still remains unexplored.In the current work,a 10310 X-ray photodetector array was constructed from single-crystal diamond.To improve the sensitivity of the diamond X-ray detector,an asymmetric sandwich electrode structure was utilized.Additionally,trenches were created through laser cutting to prevent crosstalk between adjacent pixels.The diamond X-ray detector array exhibits exceptional performance,including a low detection limit of 4.9 nGy s^(-1),a sensitivity of 14.3 mC Gy^(-1) cm^(-2),and a light-dark current ratio of 18,312,which are among the most favorable values ever reported for diamond X-ray detectors.Furthermore,these diamond X-ray detectors can operate at high temperatures up to 450℃,making them suitable for development in harsh environments.

Deep learning for the detection of semantic features in tree X-ray CT scans

According to the industry,the value of wood logs is heavily influenced by their internal structure,particularly the distribution of knots within the trees.Nowadays,CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of trees.Knowing where the tree semantic features,especially knots,contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log by-products.However,this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline,knot localization and log contour,in a robust and efficient manner.In this paper,we propose an effective methodology based on deep learning for performing these different tasks by processing CTscanner images with deep convolutional neural networks.To meet this objective,three end-to-end trainable pipelines are proposed.The first pipeline is focused on centers detection using CNNs architecture with a regression head,the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder architecture.The different architectures are tested on several tree species.With these experiments,we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of robustness.The main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.

Super‑elastic Scintillating Fibers and Fabrics for Efficient and Visual Radiation Detection

The fabrication of advanced radiation detectors is an important subject due to the wide use of radiation sources in scientific instruments,medical services,security check,non-destructive inspection,and nuclear industries.However,the manufacture of flexible and stretchable radiation detectors remains a challenge.Here,we report the scalable fabrication of super-elastic scintillating fibers and fabrics for visual radiation detection by thermal drawing and melt-spinning methods using styrene-b-(ethylene-co-butylene)-b-styrene,and scintillating Gd_(2)O_(2)S:Tb(GOS).Microstructure evolution,rheological properties,and radiation-composite interaction are studied to reveal the excellent processability,elasticity,and radiation detection ability of the fabricated fibers.Benefiting from the physical crosslinking structural features of the polymer matrix and the excellent radiation absorption capacities of GOS,the resulting fiber can sustain high strains of 765%with a high content of GOS dopants(2 wt.%)and has excellent X-ray detection performance with the limit down to 53 nGy_(air)s^(-1).Furthermore,stretchable fabrics are constructed,and their applications in various fields,such as radiation warning,and X-ray imaging,are demonstrated.Our work not only provides a new type of super-elastic scintillating fibers and fabrics for smart textiles but also demonstrates their potential applications in the nuclear field.

Structural and Piezoelectric Properties of Sr_(0.6)Ba_(0.4)Nb_2O_6 Micro-rods Synthesized by Molten-Salt Method

Sr0.6 Ba0.4 Nb2 O6 micro-rods are prepared by the molten-salt method with K2 SO4,KCl-K2 SO4,and KCl as fluxes.It reveals that the Sr0.6 Ba0.4 Nb2 O6 synthesized with KCl as a flux exhibits a single phase with tetragonal tungsten bronze structure.The measurement of X-ray diffraction indicates that the Sr0.6 Ba0.4 Nb2 O6 micro-rods synthesized at 1 300℃are anisotropic.The morphology of the powers is examined by transmission electron microscope.It reveals that the length-diameter ratio of Sr0.6 Ba0.4 Nb2 O6 micro-rods increases with increasing annealing temperature from 900℃to 1 300℃.At 1 300℃,the rod possesses a large length-diameter ratio of 8∶1.Moreover,the analysis of the piezoelectric properties of single micro-rods using apiezo-response force microscope indicates that the domains of the material are arranged along its radial direction.

Phase-contrast Imaging Simulation Based on a Micro-CT System

Propagation-based phase-contrast imaging was simulated based on paraxial Fresnel-Kirchoff diffraction integral and spherical wave illumination. Under a developed micro-CT system parameters, the effects of focal-spot size and imaging geometry on phase-contrast imaging have been investigated using a 2-mm-thickness polystyrene edge phantom. An equivalent mono-energy was used to substitute the polychromatic spectrum of the micro-focus X-ray source. To consider effects of focal-spot size and detector resolution, the obtained phase-contrast image with an ideal point source was convolved with source intensity distribution and point spread function of detector. Simulations show reasonable influences of the two parameters which are in good agreement with experimental results.