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
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
We developed a monochromatic crystal backlight imaging system for the double-cone ignition(DCI) scheme, employing a spherically bent quartz crystal. This system was used to measure the spatial distribution and tempora...We developed a monochromatic crystal backlight imaging system for the double-cone ignition(DCI) scheme, employing a spherically bent quartz crystal. This system was used to measure the spatial distribution and temporal evolution of the head-on colliding plasma from the two compressing cones in the DCI experiments. The influence of laser parameters on the x-ray backlighter intensity and spatial resolution of the imaging system was investigated. The imaging system had a spatial resolution of 10 μm when employing a CCD detector. Experiments demonstrated that the system can obtain time-resolved radiographic images with high quality, enabling the precise measurement of the shape, size, and density distribution of the plasma.展开更多
Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image a...Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.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.
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Project supported by the staff of the Shenguang-Ⅱ upgrade Laser facilityThis study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDA25010100,XDA25010300,XDA25030100,XDA25030200,and XDA25051000)+2 种基金the National Natural Science Foundation of China(Grant Nos.11827807 and 12105359)the Open Foundation of Key Laboratory of High Power Laser and Physics of Chinese Academy of Sciences(Grant No.SGKF202105)the Chinese Academy of Sciences Youth Interdisciplinary Team(Grant No.JCTD-2022-05).
文摘We developed a monochromatic crystal backlight imaging system for the double-cone ignition(DCI) scheme, employing a spherically bent quartz crystal. This system was used to measure the spatial distribution and temporal evolution of the head-on colliding plasma from the two compressing cones in the DCI experiments. The influence of laser parameters on the x-ray backlighter intensity and spatial resolution of the imaging system was investigated. The imaging system had a spatial resolution of 10 μm when employing a CCD detector. Experiments demonstrated that the system can obtain time-resolved radiographic images with high quality, enabling the precise measurement of the shape, size, and density distribution of the plasma.
文摘Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.
