Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the result...Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the resulting neutron radiographic images inevitably exhibit multiple distortions,including noise,geometric unsharpness,and white spots.Furthermore,these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes.Therefore,in this study,we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets.Thereafter,the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images.Extensive experiments were performed;the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-theart perceptual visual quality,thus demonstrating its application potential in neutron radiography.展开更多
Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest rad...Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest radiographs,diabetic retinopathy,breast cancer,skin carcinoma classification,and metastatic lymphadenopathy detection,with diagnostic reliability akin to medical experts.In the World Journal of Orthopedics article,the authors apply an automated and AIassisted technique to determine the hallux valgus angle(HVA)for assessing HV foot deformity.With the U-net neural network,the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior highresolution radiographs.The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability.The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency.Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies,this work adds to the growing evidence supporting AI as a powerful diagnostic tool.展开更多
Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can b...Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.展开更多
This paper reports on the results of calculations using a Monte Carlo code (MCNP5) to study the properties of photons, electrons and photoneutrons obtained in the converted target and their transportations in x-ray ...This paper reports on the results of calculations using a Monte Carlo code (MCNP5) to study the properties of photons, electrons and photoneutrons obtained in the converted target and their transportations in x-ray radiography. A comparison between measurements and calculations for bremsstrahlung and photoneutrons is presented. The radiographic rule and the effect of the collimator on the image are studied with the experimental model. The results provide exact parameters for the optimal design of radiographic layout and shielding systems.展开更多
Owing to the immobility of traditional reactors and spallation neutron sources,the demand for compact thermal neutron radiography(CTNR)based on accelerator neutron sources has rapidly increased in industrial applicati...Owing to the immobility of traditional reactors and spallation neutron sources,the demand for compact thermal neutron radiography(CTNR)based on accelerator neutron sources has rapidly increased in industrial applications.Recently,thermal neutron radiography experiments based on a D-T neutron generator performed by Hefei Institutes of Physical Science indicated a significant resolution deviation between the experimental results and the values calculated using the traditional resolution model.The experimental result was up to 23%lower than the calculated result,which hinders the achievement of the design goal of a compact neutron radiography system.A GEANT4 Monte Carlo code was developed to simulate the CTNR process,aiming to identify the key factors leading to resolution deviation.The effects of a low collimation ratio and high-energy neutrons were analyzed based on the neutron beam environment of the CTNR system.The results showed that the deviation was primarily caused by geometric distortion at low collimation ratios and radiation noise induced by highenergy neutrons.Additionally,the theoretical model was modified by considering the imaging position and radiation noise factors.The modified theoretical model was in good agreement with the experimental results,and the maximum deviation was reduced to 4.22%.This can be useful for the high-precision design of CTNR systems.展开更多
It is of great significance to develop clean and new energy sources with high-efficient energy storage technologies,due to the excessive use of fossil energy that has caused severe environmental damage.There is great ...It is of great significance to develop clean and new energy sources with high-efficient energy storage technologies,due to the excessive use of fossil energy that has caused severe environmental damage.There is great interest in exploring advanced rechargeable lithium batteries with desirable energy and power capabilities for applications in portable electronics,smart grids,and electric vehicles.In practice,high-capacity and low-cost electrode materials play an important role in sustaining the progresses in lithium-ion batteries.This review aims at giving an account of recent advances on the emerging high-capacity electrode materials and summarizing key barriers and corresponding strategies for the practical viability of these electrode materials.Effective approaches to enhance energy density of lithium-ion batteries are to increase the capacity of electrode materials and the output operation voltage.On account of major bottlenecks of the power lithium-ion battery,authors come up with the concept of integrated battery systems,which will be a promising future for high-energy lithium-ion batteries to improve energy density and alleviate anxiety of electric vehicles.展开更多
Computed radiography(CR)imaging has high irradiation tolerance and it is easy to archive CR images along with other image information by Digital Imaging and Communications in Medicine(DICOM)format,and to process them....Computed radiography(CR)imaging has high irradiation tolerance and it is easy to archive CR images along with other image information by Digital Imaging and Communications in Medicine(DICOM)format,and to process them.CR can be used in radiation Quality Control(QC)task and verification of treatment setting-up.In this paper,the role of high-energy CR in radiation oncology is studied.The patients were imaged by CR system and EPID before radiotherapy.All verification images were acquired with 1–2 MU(Monitor Unit)using 6 MV X-rays.QC for a linac was done with film and high-energy CR to collect the data on daily,weekly and monthly basis.The QC included Multileaf Collimators(MLC)calibration and mechanical iso-centre check.CR was also adapted to verify patient position,the film was used to compare with digitally reconstructed radiographs(DRR)and portal image from EPID. Treatment setting-up was verified based on the result of comparison.High quality verification images could be acquired by the CR system.Comparing to EPID,the results showed that the system was suitable for practical use to acquire daily verification images,and it was useful to fulfill part of quality assurance(QA)in radiation oncology.The quality of image acquired by the high-energy CR system is comparable or even better than DRRs and portal images. The final treatment set-up for the patients could be verified more accurately with the CR system.展开更多
We use the High-energy Electron Experiments(HEP)instrument onboard Arase(ERG)to conduct an energy-dependent cross-satellite calibration of electron fluxes measured by the High Energy Particle Detector(HEPD)onboard Fen...We use the High-energy Electron Experiments(HEP)instrument onboard Arase(ERG)to conduct an energy-dependent cross-satellite calibration of electron fluxes measured by the High Energy Particle Detector(HEPD)onboard FengYun-4A(FY-4A)spanning from April 1,2017,to September 30,2019.By tracing the two-dimensional magnetic positions(L,magnetic local time[MLT])of FY-4A at each time,we compare the datasets of the conjugate electron fluxes over the range of 245–894 keV in 6 energy channels for the satellite pair within different sets of L×MLT.The variations in the electron fluxes observed by FY-4A generally agree with the Arase measurements,and the percentages of the ratios of electron flux conjunctions within a factor of 2 are larger than 50%.Compared with Arase,FY-4A systematically overestimates electron fluxes at all 6 energy channels,with the corresponding calibration factors ranging from 0.67 to 0.81.After the cross-satellite calibration,the electron flux conjunctions between FY-4A and Arase show better agreement,with much smaller normalized root mean square errors.Our results provide a valuable reference for the application of FY-4A high-energy electron datasets to in-depth investigations of the Earth’s radiation belt electron dynamics.展开更多
基金supported by National Natural Science Foundation of China(Nos.11905028,12105040)Scientific Research Project of Education Department of Jilin Province(No.JJKH20231294KJ)。
文摘Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the resulting neutron radiographic images inevitably exhibit multiple distortions,including noise,geometric unsharpness,and white spots.Furthermore,these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes.Therefore,in this study,we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets.Thereafter,the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images.Extensive experiments were performed;the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-theart perceptual visual quality,thus demonstrating its application potential in neutron radiography.
文摘Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest radiographs,diabetic retinopathy,breast cancer,skin carcinoma classification,and metastatic lymphadenopathy detection,with diagnostic reliability akin to medical experts.In the World Journal of Orthopedics article,the authors apply an automated and AIassisted technique to determine the hallux valgus angle(HVA)for assessing HV foot deformity.With the U-net neural network,the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior highresolution radiographs.The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability.The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency.Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies,this work adds to the growing evidence supporting AI as a powerful diagnostic tool.
文摘Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.
基金Project supported by the National Natural Science Foundation of China (Grant No.10576006)the Foundation of China Academy of Engineering Physics (Grant Nos.2007A01001 and 2009B0202020)
文摘This paper reports on the results of calculations using a Monte Carlo code (MCNP5) to study the properties of photons, electrons and photoneutrons obtained in the converted target and their transportations in x-ray radiography. A comparison between measurements and calculations for bremsstrahlung and photoneutrons is presented. The radiographic rule and the effect of the collimator on the image are studied with the experimental model. The results provide exact parameters for the optimal design of radiographic layout and shielding systems.
基金supported by the Nuclear Energy Development Project of China (No.[2019]1342)the Presidential Foundation of HFIPS (No.YZJJ2022QN40)。
文摘Owing to the immobility of traditional reactors and spallation neutron sources,the demand for compact thermal neutron radiography(CTNR)based on accelerator neutron sources has rapidly increased in industrial applications.Recently,thermal neutron radiography experiments based on a D-T neutron generator performed by Hefei Institutes of Physical Science indicated a significant resolution deviation between the experimental results and the values calculated using the traditional resolution model.The experimental result was up to 23%lower than the calculated result,which hinders the achievement of the design goal of a compact neutron radiography system.A GEANT4 Monte Carlo code was developed to simulate the CTNR process,aiming to identify the key factors leading to resolution deviation.The effects of a low collimation ratio and high-energy neutrons were analyzed based on the neutron beam environment of the CTNR system.The results showed that the deviation was primarily caused by geometric distortion at low collimation ratios and radiation noise induced by highenergy neutrons.Additionally,the theoretical model was modified by considering the imaging position and radiation noise factors.The modified theoretical model was in good agreement with the experimental results,and the maximum deviation was reduced to 4.22%.This can be useful for the high-precision design of CTNR systems.
基金supported by National Natural Science Foundation of China(No.51902340)Chongqing Natural Science Foundation,and Chongqing Postdoctoral Science Foundation(No.2021000051).
文摘It is of great significance to develop clean and new energy sources with high-efficient energy storage technologies,due to the excessive use of fossil energy that has caused severe environmental damage.There is great interest in exploring advanced rechargeable lithium batteries with desirable energy and power capabilities for applications in portable electronics,smart grids,and electric vehicles.In practice,high-capacity and low-cost electrode materials play an important role in sustaining the progresses in lithium-ion batteries.This review aims at giving an account of recent advances on the emerging high-capacity electrode materials and summarizing key barriers and corresponding strategies for the practical viability of these electrode materials.Effective approaches to enhance energy density of lithium-ion batteries are to increase the capacity of electrode materials and the output operation voltage.On account of major bottlenecks of the power lithium-ion battery,authors come up with the concept of integrated battery systems,which will be a promising future for high-energy lithium-ion batteries to improve energy density and alleviate anxiety of electric vehicles.
基金Supported by the Municipal Health Bureau of Shanghai(Contract No.04017)
文摘Computed radiography(CR)imaging has high irradiation tolerance and it is easy to archive CR images along with other image information by Digital Imaging and Communications in Medicine(DICOM)format,and to process them.CR can be used in radiation Quality Control(QC)task and verification of treatment setting-up.In this paper,the role of high-energy CR in radiation oncology is studied.The patients were imaged by CR system and EPID before radiotherapy.All verification images were acquired with 1–2 MU(Monitor Unit)using 6 MV X-rays.QC for a linac was done with film and high-energy CR to collect the data on daily,weekly and monthly basis.The QC included Multileaf Collimators(MLC)calibration and mechanical iso-centre check.CR was also adapted to verify patient position,the film was used to compare with digitally reconstructed radiographs(DRR)and portal image from EPID. Treatment setting-up was verified based on the result of comparison.High quality verification images could be acquired by the CR system.Comparing to EPID,the results showed that the system was suitable for practical use to acquire daily verification images,and it was useful to fulfill part of quality assurance(QA)in radiation oncology.The quality of image acquired by the high-energy CR system is comparable or even better than DRRs and portal images. The final treatment set-up for the patients could be verified more accurately with the CR system.
基金supported by the National Natural Science Foundation of China(Grant Nos.42025404,42188101,42241143,41931073,and 42204160)the National Key R&D Program of China(Grant Nos.2022YFF0503700,2022YFF0503900,and 2021YFA0718600)+1 种基金the B-type Strategic Priority Program of the Chinese Academy of Sciences(Grant No.XDB41000000)the Fundamental Research Funds for the Central Universities(Grant Nos.2042022kf1012 and 2042022kf1016).
文摘We use the High-energy Electron Experiments(HEP)instrument onboard Arase(ERG)to conduct an energy-dependent cross-satellite calibration of electron fluxes measured by the High Energy Particle Detector(HEPD)onboard FengYun-4A(FY-4A)spanning from April 1,2017,to September 30,2019.By tracing the two-dimensional magnetic positions(L,magnetic local time[MLT])of FY-4A at each time,we compare the datasets of the conjugate electron fluxes over the range of 245–894 keV in 6 energy channels for the satellite pair within different sets of L×MLT.The variations in the electron fluxes observed by FY-4A generally agree with the Arase measurements,and the percentages of the ratios of electron flux conjunctions within a factor of 2 are larger than 50%.Compared with Arase,FY-4A systematically overestimates electron fluxes at all 6 energy channels,with the corresponding calibration factors ranging from 0.67 to 0.81.After the cross-satellite calibration,the electron flux conjunctions between FY-4A and Arase show better agreement,with much smaller normalized root mean square errors.Our results provide a valuable reference for the application of FY-4A high-energy electron datasets to in-depth investigations of the Earth’s radiation belt electron dynamics.
基金supported by the Natural Science Foundation of Hunan Province,China(No.2021JJ30672)the Science and Technology Project of Education Department of Hunan Province,China(No.22A0100)+1 种基金the National Natural Science Foundation of China(No.51627802)Xiangtan University Scientific Research Start-up Fund。