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X-Ray Optics|19 Article(s)
Research Progresses of Zero-Dimensional Lead-Free Hybrid Halides Scintillators for X-Ray Imaging (Invited)
Wen Li, Yunyun Li, Xiaohui Chi, and Yuntao Wu
X-ray scintillators are widely used in medical diagnosis, safety inspection, industrial non-destructive detection, and other fields. In the past decade, zero-dimensional (0D) organic-inorganic hybrid metal halides have gradually attracted attention and shown great potential in X-ray imaging due to their excellent physical properties and luminescent properties such as non-deliquescence, high stability, no self-absorption, and high luminescent quantum efficiency. This article will overview the basic detection principles and key detection performance parameters of X-ray scintillators, introduce the research progresses of the most representative 0D manganese-based, tin-based, antimony-based, and copper-based halide scintillators in the field of X-ray imaging, and look forward to the future development direction of these 0D hybrid materials. X-ray scintillators are widely used in medical diagnosis, safety inspection, industrial non-destructive detection, and other fields. In the past decade, zero-dimensional (0D) organic-inorganic hybrid metal halides have gradually attracted attention and shown great potential in X-ray imaging due to their excellent physical properties and luminescent properties such as non-deliquescence, high stability, no self-absorption, and high luminescent quantum efficiency. This article will overview the basic detection principles and key detection performance parameters of X-ray scintillators, introduce the research progresses of the most representative 0D manganese-based, tin-based, antimony-based, and copper-based halide scintillators in the field of X-ray imaging, and look forward to the future development direction of these 0D hybrid materials.
Laser & Optoelectronics Progress
- Publication Date: Feb. 10, 2024
- Vol. 61, Issue 3, 0334001 (2024)
Optimization Strategy for X-Ray Generation and Countermeasure Fusion of Bronze Mirror
Meng Wu, Jiao Wang, and Jiankai Xiang
During the non-contact flaw detection of a rust-covered bronze mirror, X-ray imaging typically fails to reveal the extent of damage due to the thickness difference between the mirror edge and core. In this study, the X-ray signal from a bronze mirror was used as an input to construct a generative confrontation fusion network. An optimization strategy that enhances the bronze mirror X-ray information fusion was designed to address the reconstruction blur caused by the L2 loss and gradient operator, and the expression of multiscale feature details, such as textures and cracks. By utilizing the feature learning process of the L2,12 loss regularization generator, the smoothing of the data that was generated using the L2 loss was improved; moreover, the Laplacian Ltex pattern loss was defined to strengthen the effect of training network on the extraction of decorations and diseases. Furthermore, a multiscale feature fusion module was added to the training network to improve the quality of the generated information. Thus, considering the experimental comparison involving seven fusion methods, the cross entropy value of the proposed algorithm in two of the five groups is poor. However, the values are optimal in the control data, including entropy, average gradient, spatial frequency, joint entropy, and non-reference feature mutual information. This can effectively reveal the detection information of the bronze mirror during X-ray flaw detection. During the non-contact flaw detection of a rust-covered bronze mirror, X-ray imaging typically fails to reveal the extent of damage due to the thickness difference between the mirror edge and core. In this study, the X-ray signal from a bronze mirror was used as an input to construct a generative confrontation fusion network. An optimization strategy that enhances the bronze mirror X-ray information fusion was designed to address the reconstruction blur caused by the L2 loss and gradient operator, and the expression of multiscale feature details, such as textures and cracks. By utilizing the feature learning process of the L2,12 loss regularization generator, the smoothing of the data that was generated using the L2 loss was improved; moreover, the Laplacian Ltex pattern loss was defined to strengthen the effect of training network on the extraction of decorations and diseases. Furthermore, a multiscale feature fusion module was added to the training network to improve the quality of the generated information. Thus, considering the experimental comparison involving seven fusion methods, the cross entropy value of the proposed algorithm in two of the five groups is poor. However, the values are optimal in the control data, including entropy, average gradient, spatial frequency, joint entropy, and non-reference feature mutual information. This can effectively reveal the detection information of the bronze mirror during X-ray flaw detection.
Laser & Optoelectronics Progress
- Publication Date: Jan. 25, 2023
- Vol. 60, Issue 2, 0234001 (2023)
Visual Inspection of Food Packaging Paper by X-Ray Fluorescence Spectroscopy Combined with Deep Learning Algorithm
Qi Guo, Hong Jiang, Jinjie Yang, Kenan Wu, and Ji Man
To quickly classify and identify common food packaging paper at the scene of the case, a visual inspection method of food packaging paper based on X-ray fluorescence spectroscopy (XRF) and deep learning algorithm is proposed. First, the inorganic elements in 44 samples of food packaging paper from different sources were detected via XRF, and artificial classification and cluster analysis were performed based on the content of the main constituent elements. Second, to test the clustering effect and visualize data classification, two-dimensionality reduction algorithms, principal component analysis, and t-distribution random neighborhood embedding are used. Finally, 80% of the samples are randomly selected as the training set to construct the artificial neural network , and relevant experiments are carried out. The experimental results show that classification accuracy of the proposed method on the test set is 88.9%, which can be used as a reference for future practical applications of public security business. To quickly classify and identify common food packaging paper at the scene of the case, a visual inspection method of food packaging paper based on X-ray fluorescence spectroscopy (XRF) and deep learning algorithm is proposed. First, the inorganic elements in 44 samples of food packaging paper from different sources were detected via XRF, and artificial classification and cluster analysis were performed based on the content of the main constituent elements. Second, to test the clustering effect and visualize data classification, two-dimensionality reduction algorithms, principal component analysis, and t-distribution random neighborhood embedding are used. Finally, 80% of the samples are randomly selected as the training set to construct the artificial neural network , and relevant experiments are carried out. The experimental results show that classification accuracy of the proposed method on the test set is 88.9%, which can be used as a reference for future practical applications of public security business.
Laser & Optoelectronics Progress
- Publication Date: Feb. 20, 2022
- Vol. 59, Issue 4, 0434001 (2022)
Prediction Model of Aluminized Layer Thickness Based on X-Ray Fluorescence and Extreme Gradient Boosting
Zhuoyue Li, Cheng Wang, Qiuliang Li, Zhenping Guo, Bin Li, and Xin Li
As an important means of high-temperature protection for aero-engine turbine blades, the quality of aluminized coatings is closely related to flight safety. The thickness of the aluminized layer is an essential factor in evaluating its performance. However, it is not easy to measure it accurately by current nondestructive testing methods. For this problem, the X-ray fluorescence technology is combined with the extreme gradient boosting (XGBoost) algorithm, and the feature element extraction by Pearson correlation coefficient screening (PCCS) is used to build a prediction model for the thickness of the aluminized layer. The average relative error of the prediction results is compared with K nearest neighbor regression, linear regression, support vector machine, and random forest models. The results show that the PCCS-XGBoost model had the smallest average error of 1.60% in predicting thickness compared with other models. The study provides a new prediction method for nondestructive testing of the thickness of the aluminized layer. As an important means of high-temperature protection for aero-engine turbine blades, the quality of aluminized coatings is closely related to flight safety. The thickness of the aluminized layer is an essential factor in evaluating its performance. However, it is not easy to measure it accurately by current nondestructive testing methods. For this problem, the X-ray fluorescence technology is combined with the extreme gradient boosting (XGBoost) algorithm, and the feature element extraction by Pearson correlation coefficient screening (PCCS) is used to build a prediction model for the thickness of the aluminized layer. The average relative error of the prediction results is compared with K nearest neighbor regression, linear regression, support vector machine, and random forest models. The results show that the PCCS-XGBoost model had the smallest average error of 1.60% in predicting thickness compared with other models. The study provides a new prediction method for nondestructive testing of the thickness of the aluminized layer.
Laser & Optoelectronics Progress
- Publication Date: Nov. 10, 2022
- Vol. 59, Issue 21, 2134001 (2022)
Large Angle Positioning Error Correction for Sub-Aperture Stitching Interferometry of Cylindrical X-Ray Mirrors
Die Qin, Yongqian Wu, Yan Xu, Shuai Zhang, and Ting Deng
In order to solve the mechanical positioning error of sub-aperture in stitching interferometric measurement, we propose a mechanical error compensation method based on genetic algorithm. The algorithm uses the matching degree of sub-aperture overlapping area as the fitness function, and then uses the error search algorithm to calculate and compensate the positioning error generated in the process of sub-aperture measurement. Through simulation and experimental verification, it is proved that the algorithm can compensate the sub-aperture mechanical positioning error. The simulation results show that the angle error calculation accuracy of the algorithm is better than 0.01°, and the displacement error calculation accuracy is better than 0.16 mm. The stitching surface after compensation is basically consistent with the simulation surface. The experimental results of stitching measurement of an elliptical cylindrical mirror with the curvature radius of 100 m show that the mechanical error compensation algorithm proposed can effectively compensate the mechanical positioning error introduced in the process of stitching measurement, and reduce the dependence of sub-aperture measurement on the accuracy of mechanical displacement table. In order to solve the mechanical positioning error of sub-aperture in stitching interferometric measurement, we propose a mechanical error compensation method based on genetic algorithm. The algorithm uses the matching degree of sub-aperture overlapping area as the fitness function, and then uses the error search algorithm to calculate and compensate the positioning error generated in the process of sub-aperture measurement. Through simulation and experimental verification, it is proved that the algorithm can compensate the sub-aperture mechanical positioning error. The simulation results show that the angle error calculation accuracy of the algorithm is better than 0.01°, and the displacement error calculation accuracy is better than 0.16 mm. The stitching surface after compensation is basically consistent with the simulation surface. The experimental results of stitching measurement of an elliptical cylindrical mirror with the curvature radius of 100 m show that the mechanical error compensation algorithm proposed can effectively compensate the mechanical positioning error introduced in the process of stitching measurement, and reduce the dependence of sub-aperture measurement on the accuracy of mechanical displacement table.
Laser & Optoelectronics Progress
- Publication Date: Sep. 10, 2022
- Vol. 59, Issue 17, 1734002 (2022)
Effect of X-Ray Energy on X-Ray CsI(Tl) Scintillation Screen Based on Macroporous Silicon
Shuangshuang Wang, Chunyang Liu, Guozheng Wang, and Xulei Qin
A model of X-ray CsI(Tl) scintillation screen based on the macroporous silicon is established, and the influence of X-ray energy on the X-ray CsI (Tl) scintillation screen’s light output is simulated and analyzed. An X-ray scintillation screen with a period of 10 μm and a side length of 8 μm was prepared, and an X-ray imaging device was constructed. The X-ray images of different X-ray source-tube voltages were measured. The light output of the X-ray scintillation screen was analyzed using MATLAB software. The results show that an increase in the thickness of the scintillation screen increases the average gray value of the scintillation screen, which is consistent with the simulation results. A model of X-ray CsI(Tl) scintillation screen based on the macroporous silicon is established, and the influence of X-ray energy on the X-ray CsI (Tl) scintillation screen’s light output is simulated and analyzed. An X-ray scintillation screen with a period of 10 μm and a side length of 8 μm was prepared, and an X-ray imaging device was constructed. The X-ray images of different X-ray source-tube voltages were measured. The light output of the X-ray scintillation screen was analyzed using MATLAB software. The results show that an increase in the thickness of the scintillation screen increases the average gray value of the scintillation screen, which is consistent with the simulation results.
Laser & Optoelectronics Progress
- Publication Date: Sep. 10, 2022
- Vol. 59, Issue 17, 1734001 (2022)
Prediction of Cr, Mn, and Ni in Medium and Low Alloy Steels by GA-BP Neural Network Combined with EDXRF Technology
Haisheng Song, Zhao Chen, Dacheng Xu, and Rongwang Xu
The Cr, Mn, and Ni content of medium and low alloy steel were analyzed using energy dispersive X-ray fluorescence spectroscopy (EDXRF) and black propagation neural network optimized by genetic algorithm (GA-BP). EDXRF was used to excite the six standard samples of medium and low alloy steel and the X-ray fluorescence spectra were obtained. The characteristic peak intensity of each element was obtained by subtracting the background using the two-point method. A total of 108 groups of spectral data and their corresponding content-based GA-BP neural network were obtained. To forecast the contents of 36 low alloy steel samples, the training completion of the GA-BP neural network was used. The predicted results and the fundamental parameter method analysis results were compared. The average errors of the chemical analysis results of the standard samples were 0.0287%, 0.0314%, and 0.0423% for Cr, Mn, and Ni, respectively. The experimental results showed that the BP neural network optimized by the genetic algorithm is suitable for the EDXRF analysis of Cr, Mn, and Ni in medium and low alloy steel. The Cr, Mn, and Ni content of medium and low alloy steel were analyzed using energy dispersive X-ray fluorescence spectroscopy (EDXRF) and black propagation neural network optimized by genetic algorithm (GA-BP). EDXRF was used to excite the six standard samples of medium and low alloy steel and the X-ray fluorescence spectra were obtained. The characteristic peak intensity of each element was obtained by subtracting the background using the two-point method. A total of 108 groups of spectral data and their corresponding content-based GA-BP neural network were obtained. To forecast the contents of 36 low alloy steel samples, the training completion of the GA-BP neural network was used. The predicted results and the fundamental parameter method analysis results were compared. The average errors of the chemical analysis results of the standard samples were 0.0287%, 0.0314%, and 0.0423% for Cr, Mn, and Ni, respectively. The experimental results showed that the BP neural network optimized by the genetic algorithm is suitable for the EDXRF analysis of Cr, Mn, and Ni in medium and low alloy steel.
Laser & Optoelectronics Progress
- Publication Date: Jun. 25, 2022
- Vol. 59, Issue 12, 1234001 (2022)
X-Ray Focusing Characteristics of Meridional Lobster-Eye Lens
Han Yeming, Fu Yuegang, Ouyang Mingzhao, Hu Yuan, Qin Tianling, and Li Yang
The meridional lobster-eye lens has a better focusing ability than the square-channel lobster-eye lens. In order to study the influence of the characteristics of the reflective surface of the meridional lobster-eye lens on the X-ray focusing imaging characteristics, one ray tracing algorithm for the meridional lobster-eye lens is established based on the principle of prism expansion. In addition, the effects of coating material, coating thickness and surface roughness on the focusing characteristics of X-rays with different energies are analyzed based on the Fresnel reflection principle. The research results show that the X-ray focusing ability of the meridional lobster-eye lens is mainly determined by the transfer efficiency of the reflective surface, and the transfer efficiency is determined by the chosen coating thickness and coating roughness and simultaneously modulated by the channel-cone apex angle. The X-ray energy determines the effective object aperture angle of the meridional lobster eye lens, and the matching of the lens field of view with the effective object aperture angle also influences its focusing ability and focal spot distribution. The meridional lobster-eye lens has a better focusing ability than the square-channel lobster-eye lens. In order to study the influence of the characteristics of the reflective surface of the meridional lobster-eye lens on the X-ray focusing imaging characteristics, one ray tracing algorithm for the meridional lobster-eye lens is established based on the principle of prism expansion. In addition, the effects of coating material, coating thickness and surface roughness on the focusing characteristics of X-rays with different energies are analyzed based on the Fresnel reflection principle. The research results show that the X-ray focusing ability of the meridional lobster-eye lens is mainly determined by the transfer efficiency of the reflective surface, and the transfer efficiency is determined by the chosen coating thickness and coating roughness and simultaneously modulated by the channel-cone apex angle. The X-ray energy determines the effective object aperture angle of the meridional lobster eye lens, and the matching of the lens field of view with the effective object aperture angle also influences its focusing ability and focal spot distribution.
Laser & Optoelectronics Progress
- Publication Date: Mar. 01, 2021
- Vol. 58, Issue 6, 634001 (2021)
Effect of Different Substrate Materials on Focusing Beam Performance of EUV Collector
Wanlu Xie, Xiaobin Wu, Kuibo Wang, Yan Luo, and Yu Wang
The effects of different substrate materials on the focusing performance of the collecting mirror in the extreme ultraviolet (EUV) radiation-damage-test system including fused silica, SiC and AlSi are simulated and analyzed. Compared with ceramic substrates such as fused silica and SiC, the thermal conductivity of AlSi-based collector mirrors is higher, but in the EUV irradiation damage research system, the maximum temperature rise of the surface of the collector on the AlSi substrate is 1.39 ℃ when the power of 10 W is applied to the 300 mm diameter collector. Although AlSi has larger expansion coefficient than that of fused silica and SiC, the maximum structural deformation of the reflector is 3.51 μm, and the wavefront aberration is increased by 8.071λ and the root mean square radius of the spot is increased by 0.028 μm compared with the ideal non-deformation collector, but the AlSi based collectors can be used in non-imaging systems or systems with low requirements for imaging quality, it has obvious advantage for collectors with large aperture and complex surface. The effects of different substrate materials on the focusing performance of the collecting mirror in the extreme ultraviolet (EUV) radiation-damage-test system including fused silica, SiC and AlSi are simulated and analyzed. Compared with ceramic substrates such as fused silica and SiC, the thermal conductivity of AlSi-based collector mirrors is higher, but in the EUV irradiation damage research system, the maximum temperature rise of the surface of the collector on the AlSi substrate is 1.39 ℃ when the power of 10 W is applied to the 300 mm diameter collector. Although AlSi has larger expansion coefficient than that of fused silica and SiC, the maximum structural deformation of the reflector is 3.51 μm, and the wavefront aberration is increased by 8.071λ and the root mean square radius of the spot is increased by 0.028 μm compared with the ideal non-deformation collector, but the AlSi based collectors can be used in non-imaging systems or systems with low requirements for imaging quality, it has obvious advantage for collectors with large aperture and complex surface.
Laser & Optoelectronics Progress
- Publication Date: Aug. 31, 2020
- Vol. 57, Issue 17, 173401 (2020)
Application of Image Enhancement in X-Ray Photography of Cultural Relics
Jiankai Xiang, Meng Wu, Zhan Wang, Gang Zhen, and Linyan Ma
Based on the high-frequency emphasis filtering and adaptive histogram algorithm, the digital image enhancement technology is introduced into the X-ray images of the cultural relics with reference to the medical X-ray image processing method. After image enhancement, the edges and details of the X-ray image of an artifact are enhanced, the contrast is stretched, and the useful information is clear. The results show that the proposed method not only facilitates the image analysis, but also simplifies the entire inspection process. Based on the high-frequency emphasis filtering and adaptive histogram algorithm, the digital image enhancement technology is introduced into the X-ray images of the cultural relics with reference to the medical X-ray image processing method. After image enhancement, the edges and details of the X-ray image of an artifact are enhanced, the contrast is stretched, and the useful information is clear. The results show that the proposed method not only facilitates the image analysis, but also simplifies the entire inspection process.
Laser & Optoelectronics Progress
- Publication Date: Mar. 14, 2019
- Vol. 56, Issue 6, 063402 (2019)
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