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.

Perovskite semiconductors for direct X-ray detection and imaging

Halide perovskites have emerged as the next generation of optoelectronic materials and their remarkable performances have been attractive in the fields of solar cells,light-emitting diodes,photodetectors,etc.In addition,halide perovskites have been reported as an attractive new class of X-ray direct detecting materials recently,owning to the strong X-ray stopping capacity,excellent carrier transport,high sensitivity,and cost-effective manufacturing.Meanwhile,perovskite based direct Xray imagers have been successfully demonstrated as well.In this review article,we firstly introduced some fundamental principles of direct X-ray detection and imaging,and summarized the advances of perovskite materials for these purposes and finally put forward some needful and feasible directions.

Ultrasensitive and stable X-ray detection using zero-dimensional lead-free perovskites

Sensitive and reliable X-ray detectors are essential for medical radiography,industrial inspection and security screening.Lowering the radiation dose allows reduced health risks and increased frequency and fidelity of diagnostic technologies for earlier detection of disease and its recurrence.Three-dimensional(3 D)organic-inorganic hybrid lead halide perovskites are promising for direct X-ray detection-they show improved sensitivity compared to conventional X-ray detectors.However,their high and unstable dark current,caused by ion migration and high dark carrier concentration in the 3 D hybrid perovskites,limits their performance and long-term operation stability.Here we report ultrasensitive,stable X-ray detectors made using zero-dimensional(0 D)methylammonium bismuth iodide perovskite(MA3Bi2I9)single crystals.The 0 D crystal structure leads to a high activation energy(Ea)for ion migration(0.46 e V)and is also accompanied by a low dark carrier concentration(~10^6 cm^-3).The X-ray detectors exhibit sensitivity of 10,620μC Gy-1 air cm-2,a limit of detection(Lo D)of 0.62 nG yairs-1,and stable operation even under high applied biases;no deterioration in detection performance was observed following sensing of an integrated X-ray irradiation dose of^23,800 m Gyair,equivalent to>200,000 times the dose required for a single commercial X-ray chest radiograph.Regulating the ion migration channels and decreasing the dark carrier concentration in perovskites provide routes for stable and ultrasensitive X-ray detectors.

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.

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.

Lead-free molecular one-dimensional perovskite for efficient X-ray detection

In recent years,great progress has been achieved for organicinorganic halide perovskites due to their excellent optoelectronic properties and stability for photovoltaics,light emitting diodes,and high-energy radiation detection[1-5].One-dimensional(1D)perovskites,as an important derivative of three-dimensional(3D)perovskites,exhibit low exciton dissociation efficiency,which can produce strong quantum confinement and form self-trapping excited state[6],In addition,the hydrophobic properties and the inhibition of ion migration from large organic cations improve the moisture and thermal stability for optoelectronic devices.

Scanning electron microscopy coupled with energydispersive X-ray spectrometry for quick detection of sulfuroxidizing bacteria in environmental water samples

Detection of sulfur-oxidizing bacteria has largely been dependent on targeted gene sequencing technology or traditional cell cultivation, which usually takes from days to months to carry out. This clearly does not meet the requirements of analysis for time-sensitive samples and/or complicated environmental samples. Since energy-dispersive X-ray spectrometry(EDS) can be used to simultaneously detect multiple elements in a sample, including sulfur, with minimal sample treatment, this technology was applied to detect sulfur-oxidizing bacteria using their high sulfur content within the cell. This article describes the application of scanning electron microscopy imaging coupled with EDS mapping for quick detection of sulfur oxidizers in contaminated environmental water samples, with minimal sample handling. Scanning electron microscopy imaging revealed the existence of dense granules within the bacterial cells, while EDS identified large amounts of sulfur within them. EDS mapping localized the sulfur to these granules. Subsequent 16S rRNA gene sequencing showed that the bacteria detected in our samples belonged to the genus Chromatium, which are sulfur oxidizers. Thus, EDS mapping made it possible to identify sulfur oxidizers in environmental samples based on localized sulfur within their cells, within a short time(within 24 h of sampling). This technique has wide ranging applications for detection of sulfur bacteria in environmental water samples.

X-ray detection based on complementary metal-oxide-semiconductor sensors

Complementary metal-oxide-semiconductor(CMOS) sensors can convert X-rays into detectable signals; therefore, they are powerful tools in X-ray detection applications. Herein, we explore the physics behind X-ray detection performed using CMOS sensors. X-ray measurements were obtained using a simulated positioner based on a CMOS sensor, while the X-ray energy was modified by changing the voltage, current, and radiation time. A monitoring control unit collected video data of the detected X-rays. The video images were framed and filtered to detect the effective pixel points(radiation spots).The histograms of the images prove there is a linear relationship between the pixel points and X-ray energy. The relationships between the image pixel points, voltage, and current were quantified, and the resultant correlations were observed to obey some physical laws.

Real-Time Anomaly Detection in Packaged Food X-Ray Images Using Supervised Learning

Physical contamination of food occurs when it comes into contact with foreign objects.Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking.Therefore,a preventive method that can detect and remove foreign objects in advance is required.Several studies have attempted to detect defective products using deep learning networks.Because it is difficult to obtain foreign object-containing food data from industry,most studies on industrial anomaly detection have used unsupervised learning methods.This paper proposes a new method for real-time anomaly detection in packaged food products using a supervised learning network.In this study,a realistic X-ray image training dataset was constructed by augmenting foreign objects with normal product images in a cut-paste manner.Based on the augmented training dataset,we trained YOLOv4,a real-time object detection network,and detected foreign objects in the test data.We evaluated this method on images of pasta,snacks,pistachios,and red beans under the same conditions.The results show that the normal and defective products were classified with an accuracy of at least 94%for all packaged foods.For detecting foreign objects that are typically difficult to detect using the unsupervised learning and traditional methods,the proposed method achieved high-performance realtime anomaly detection.In addition,to eliminate the loss in high-resolution X-ray images,the false positive rate and accuracy could be lowered to 5%with patch-based training and a new post-processing algorithm.

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.

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Because of its better automated feature extraction capability,convolutional neural net-works(CNNs)trained on natural images are particularly effective in image cate-gorization.A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets.Ten different deep CNNs(Resnet50,Resnet101,Resnet152,InceptionV3,VGG16,VGG19,DenseNet121,DenseNet169,DenseNet201,MobileNet)are trained and tested for identifying TB and normal cases.This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmenta-tion of interest,and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection.Furthermore,this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs.It was able to accurately detect TB from CXR images using pre-processing,data augmentation,and deep CNN models.Without histogram matching the best accuracy,sensitivity,specificity,precision and F1-score in the detection of TB using CXR images among ten models are 99.25%,99.48%,99.52%,99.48%and 99.22%respectively.With histogram matching the best accuracy,sensitivity,specificity,precision and F1-score are 99.58%,99.82%,99.67%,99.65%and 99.56%respectively.The proposed meth-odology,which has cutting-edge performance,will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

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.

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.

Research on the Influence of X-ray Phase Contrast Imaging Parameters with Micro-focus Source

X-ray in-line phase contrast imaging enables weakly to absorb specimens to be imaged successfully with high resolution and definition. In this paper we use computer simulation method to analyze how each parameter influences the quality of the image. It can avoid wasting unnecessary time and materials in the course of experiment to get ideal images.

Readout electronics for a high-resolution soft X-ray spectrometer based on silicon drift detector

The readout electronics for a prototype soft X-ray spectrometer based on silicon drift detector(SDD),for precisely measuring the energy and arrival time of X-ray photons is presented in this paper.The system mainly consists of two parts,i.e.,an analog electronics section(including a pre-amplifier,a signal shaper and filter,a constant fraction timing circuit,and a peak hold circuit)and a digital electronics section(including an ADC and a TDC).Test results with X-ray sources show that an energy dynamic range of 1-10 keV with an integral nonlinearity of less than 0.1%can be achieved,and the energy resolution is better than 160 eV @ 5.9 keV FWHM.Using a waveform generator,test results also indicate that time resolution of the electronics system is about 3.7 ns,which is much less than the transit time spread of SDD(<100 ns)and satisfies the requirements of future applications.

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.

Direct Conversion X-Ray Detectors with High Sensitivity at Low Dose Rate Based on All-Inorganic Lead-Free Perovskite Wafers

Just as lead-based perovskites that are hot in solar cell preparation, Bi-based perovskites have demonstrated excellent performance in direct X-ray detection, especially the Cs3Bi2I9 single crystals (SCs). However, compared with lead-halide perovskites, one challenge for the Cs3Bi2I9 SCs for X-ray detection application is that it is difficult to prepare large-sized and high-quality SCs. Therefore, how to get a large area with a high-quality wafer is also as important as Cs3Bi2I9 growth method research. Here, different anti-solvents are used for the preparation of poly-crystalline powder with the Antisolvents precipitation (A) method, as a control, High-energy ball milling (B) was also used to prepare poly-crystalline powders. The resultant two types of Cs3Bi2I9 wafer exhibit a micro-strain of 1.21 × 10-3, a resistivity of 5.13 × 108 Ω cm and a microstrain of 1.21 × 10-3, a resistivity of 2.21 × 109 Ω cm. As a result, an X-ray detector based on the high-quality Cs3Bi2I9 wafer exhibits excellent dose rate linearity, a sensitivity of 588 μC·Gyairs-1·cm-2 and a limit of detection (LoD) of 76 nGyair·s-1.

Organic X-Ray Image Sensors Using a Medium Bandgap Polymer Donor with Low Dark Current

The development of portable X-ray detectors is necessary for diagnosing fractures in unconscious patients in emergency situations.However,this is quite challenging because of the heavy weight of the scintillator and silicon photodetectors.The weight and thickness of X-ray detectors can be reduced by replacing the silicon layer with an organic photodetectors.This study presents a novel bithienopyrroledione-based polymer donor that exhibits excellent photodetection properties even in a thick photoactive layer(~700 nm),owing to the symmetric backbone and highly soluble molecular structure of bithienopyrroledione.The ability of bithienopyrroledione-based polymer donor to strongly suppress the dark current density(Jd~10−10 A cm^(−2))at a negative bias(−2.0 V)while maintaining high responsivity(R=0.29 A W−1)even at a thickness of 700 nm results in a maximum shot-noise-limited specific detectivity of D_(sh)^(*)=2.18×10^(13)Jones in the organic photodetectors.Printed organic photodetectors are developed by slot-die coating for use in X-ray detectors,which exhibit D_(sh)^(*)=2.73×10^(12)Jones with clear rising(0.26 s)and falling(0.29 s)response times upon X-ray irradiation.Detection reliability is also proven by linear response of the X-ray detector,and the X-ray detection limit is 3 mA.