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,bu...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.展开更多
In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.De...In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.展开更多
Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological i...Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.展开更多
Objective:To analyze the characteristics,dynamic changes,and outcomes of the first imaging manifestations of 3 patients with severe COVID-19 in our hospital.Methods:Computed tomography(CT)findings of 3 patients with s...Objective:To analyze the characteristics,dynamic changes,and outcomes of the first imaging manifestations of 3 patients with severe COVID-19 in our hospital.Methods:Computed tomography(CT)findings of 3 patients with severe COVID-19 who tested positive by the nucleic acid test in our hospital were selected,mainly focusing on the morphology,distribution characteristics,and dynamic changes of the first CT findings.Results:3 patients with severe pneumonia were older,with one aged 80.The first chest CT examination for all 3 patients differed.Imaging showed a leafy distribution of consolidation,primarily affecting the lower lobes of both lungs and extending subpleurally.A grid-like pattern was observed,along with changes in the consolidation and air bronchogram.These changes had slower absorption,especially in patients with underlying diseases.Conclusion:CT manifestations of severe COVID-19 have specific characteristics and the analysis of their characteristics and dynamic changes provide valuable insights for clinical treatment.展开更多
Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admi...Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admitted to the Department of Orthopedics of our hospital from January 2023 to January 2024 were selected and randomly divided into a single group(n=65)and a joint group(n=65).The single group was diagnosed using multi-slice spiral CT,and the joint group was diagnosed using multi-slice spiral CT and magnetic resonance imaging,with pathological diagnosis as the gold standard.The diagnostic results of both groups were compared to the gold standard,and the diagnostic energy efficiency of both groups was compared.Results:The diagnostic results of the single group compared with the gold standard were significant(P<0.05).The diagnostic results of the joint group compared with the gold standard were not significant(P>0.05).The sensitivity and accuracy of diagnosis in the joint group were significantly higher than that in the single group(P<0.05).The specificity of diagnosis in the joint group was higher as compared to that in the single group(P>0.05).Conclusion:The combination of multi-slice spiral CT and MRI was highly accurate in diagnosing wrist injuries,and the misdiagnosis rate and leakage rate were relatively low.Hence,this diagnostic program is recommended to be popularized.展开更多
Liver cancer is one of the major diseases with increased mortality in recent years,across the globe.Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models hav...Liver cancer is one of the major diseases with increased mortality in recent years,across the globe.Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its stages.Besides,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response moni-toring.Hence,there is a need exists to develop automated tools for liver cancer detection in a precise manner.With this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)model.The proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation process.Besides,Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver.Moreover,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature vectors.At last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer effectively.In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different measures.The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.展开更多
In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to in...In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.展开更多
Background Identifying the transmural extent of myocardial necrosis and the degree of myocardial viability in acute myocardial infarction （AMI） is important clinically. The aim of this study was to assess myocardial...Background Identifying the transmural extent of myocardial necrosis and the degree of myocardial viability in acute myocardial infarction （AMI） is important clinically. The aim of this study was to assess myocardial viability using two-dimensional speckle tracking imaging （2D-STI） in patients with AMI. Methods 2D-STI was performed at initial presentation, three days, and six months after primary percutaneous coronary intervention （PCI） in 30 patients with AMI, who had a left anterior descending coronary artery （LAD） culprit lesion. In addition, 20 patients who had minimal stenotic lesions （〈 30% stenosis） on coronary angiography were also included in the control group. At six months dobutamine echocardiography was performed for viability assessment in seven segments of the LAD territory. According to the recovery of wall motion abnormality, segments were classified as viable or non-viable. Results A total of 131 segments were viable, and 44 were nonviable. Multivariate analysis revealed significant differences between the viable and nonviable segments in the peak systolic strain, the peak systolic strain rate at initial presentation, and peak systolic strain rate three days after primary PCI. Among these, the initial peak systolic strain rate had the highest predictive value for myocardial viability （hazard ratio： 31.22, P 〈 0.01）. Conclusions 2D-STI is feasible for assessing myocardial viability, and the peak systolic strain rate might be the most reliable predictor of myocardial viability in patients with AMI.展开更多
We propose a new fractional two-dimensional triangle function combination discrete chaotic map(2D-TFCDM)with the discrete fractional difference.Moreover,the chaos behaviors of the proposed map are observed and the bif...We propose a new fractional two-dimensional triangle function combination discrete chaotic map(2D-TFCDM)with the discrete fractional difference.Moreover,the chaos behaviors of the proposed map are observed and the bifurcation diagrams,the largest Lyapunov exponent plot,and the phase portraits are derived,respectively.Finally,with the secret keys generated by Menezes-Vanstone elliptic curve cryptosystem,we apply the discrete fractional map into color image encryption.After that,the image encryption algorithm is analyzed in four aspects and the result indicates that the proposed algorithm is more superior than the other algorithms.展开更多
Objective: To research the clinical application of two-dimensional speckle tracking imaging (2D-STI) in quantitative assessment of left ventricular function in patients with obstructive sleep apnea-hypopnea syndrome (...Objective: To research the clinical application of two-dimensional speckle tracking imaging (2D-STI) in quantitative assessment of left ventricular function in patients with obstructive sleep apnea-hypopnea syndrome (OSAS). Method: From July 2016 to December 2018, 86 patients with OSAS were selected as OSAS group. According to sleep apnea hypopnea index (AHI), they were divided into mild OSAS group (24 cases), moderate OSAS group (29 cases) and severe OSAS group (33 cases). Another 50 healthy volunteers who underwent physical examination in our hospital during the same period were selected as the control group. The left ventricular function of all patients was quantitatively assessed by 2D-STI. The left ventricular function of all patients was quantitatively assessed by 2D-STI. The results of routine echocardiography and left ventricular global strain parameters of the OSAS group, the control group and the OSAS patients with different severity were compared and analyzed. Result: There were no significant differences in the levels of left ventricular ejection fraction (LVEF), left ventricular end diastolic diameter (LVEDd), left ventricular end systolic diameter (LVESd) between the two groups and OSAS patients with different severity (P>0.05). The levels of IVST, LVPW and LVMI in the OSAS group were significantly higher than those in the control group, the levels of end-diastolic interventricular septal thickness (IVS), left ventricular posterior wall thickness (LVPW), left ventricular mass index (LVMI) in the severe OSAS group were significantly higher than those in the mild and moderate OSAS group, and the levels of IVST, LVPW and LVMI in the moderate OSAS group were significantly higher than those in the mild OSAS group, there were significant differences between groups (P<0.05). The levels of GLS, GRS and GCS in the OSAS group were significantly lower than those in the control group (P<0.05). GLS, GRS and GCS levels in the severe OSAS group were significantly lower than those in the mild OSAS group and the moderate OSAS group, while the levels of global longitudinal strain (GLS), global radial strain (GRS) and global circumferential strain (GCS) in the moderate OSAS group were significantly lower than those in the mild OSAS group (P<0.05). Conclusion: The left ventricular systolic function of OSAS patients is obviously impaired. Left ventricular function in OSAS patients can be assessed timely and accurately by two-dimensional speckle tracking imaging.展开更多
基金financial support from the National Natural Science Foundation of China(Nos.22075284,51872287,and U2030118)the Youth Innovation Promotion Association CAS(No.2019304)+1 种基金the Fund of Mindu Innovation Laboratory(No.2021ZR201)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(No.YJKYYQ20210039)
文摘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.
基金the Deanship for Research Innovation,Ministry of Education in Saudi Arabia,for funding this research work through project number IFKSUDR-H122.
文摘In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.
基金Supporting this research through Taif University Researchers Supporting Project number(TURSP-2020/231),Taif University,Taif,Saudi Arabia.
文摘Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.
基金Qinghai Provincial Health Commission Medical and Health Science and Technology Project Guiding Topics“Analysis of Dynamic Changes in Chest Imaging of New Coronavirus Pneumonia in Qinghai Province”(2022-wjzdx-63)。
文摘Objective:To analyze the characteristics,dynamic changes,and outcomes of the first imaging manifestations of 3 patients with severe COVID-19 in our hospital.Methods:Computed tomography(CT)findings of 3 patients with severe COVID-19 who tested positive by the nucleic acid test in our hospital were selected,mainly focusing on the morphology,distribution characteristics,and dynamic changes of the first CT findings.Results:3 patients with severe pneumonia were older,with one aged 80.The first chest CT examination for all 3 patients differed.Imaging showed a leafy distribution of consolidation,primarily affecting the lower lobes of both lungs and extending subpleurally.A grid-like pattern was observed,along with changes in the consolidation and air bronchogram.These changes had slower absorption,especially in patients with underlying diseases.Conclusion:CT manifestations of severe COVID-19 have specific characteristics and the analysis of their characteristics and dynamic changes provide valuable insights for clinical treatment.
文摘Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admitted to the Department of Orthopedics of our hospital from January 2023 to January 2024 were selected and randomly divided into a single group(n=65)and a joint group(n=65).The single group was diagnosed using multi-slice spiral CT,and the joint group was diagnosed using multi-slice spiral CT and magnetic resonance imaging,with pathological diagnosis as the gold standard.The diagnostic results of both groups were compared to the gold standard,and the diagnostic energy efficiency of both groups was compared.Results:The diagnostic results of the single group compared with the gold standard were significant(P<0.05).The diagnostic results of the joint group compared with the gold standard were not significant(P>0.05).The sensitivity and accuracy of diagnosis in the joint group were significantly higher than that in the single group(P<0.05).The specificity of diagnosis in the joint group was higher as compared to that in the single group(P>0.05).Conclusion:The combination of multi-slice spiral CT and MRI was highly accurate in diagnosing wrist injuries,and the misdiagnosis rate and leakage rate were relatively low.Hence,this diagnostic program is recommended to be popularized.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia has funded this project,under grant no.(FP-206-43).
文摘Liver cancer is one of the major diseases with increased mortality in recent years,across the globe.Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its stages.Besides,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response moni-toring.Hence,there is a need exists to develop automated tools for liver cancer detection in a precise manner.With this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)model.The proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation process.Besides,Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver.Moreover,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature vectors.At last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer effectively.In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different measures.The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.
文摘In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.
文摘Background Identifying the transmural extent of myocardial necrosis and the degree of myocardial viability in acute myocardial infarction （AMI） is important clinically. The aim of this study was to assess myocardial viability using two-dimensional speckle tracking imaging （2D-STI） in patients with AMI. Methods 2D-STI was performed at initial presentation, three days, and six months after primary percutaneous coronary intervention （PCI） in 30 patients with AMI, who had a left anterior descending coronary artery （LAD） culprit lesion. In addition, 20 patients who had minimal stenotic lesions （〈 30% stenosis） on coronary angiography were also included in the control group. At six months dobutamine echocardiography was performed for viability assessment in seven segments of the LAD territory. According to the recovery of wall motion abnormality, segments were classified as viable or non-viable. Results A total of 131 segments were viable, and 44 were nonviable. Multivariate analysis revealed significant differences between the viable and nonviable segments in the peak systolic strain, the peak systolic strain rate at initial presentation, and peak systolic strain rate three days after primary PCI. Among these, the initial peak systolic strain rate had the highest predictive value for myocardial viability （hazard ratio： 31.22, P 〈 0.01）. Conclusions 2D-STI is feasible for assessing myocardial viability, and the peak systolic strain rate might be the most reliable predictor of myocardial viability in patients with AMI.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61072147 and 11271008)
文摘We propose a new fractional two-dimensional triangle function combination discrete chaotic map(2D-TFCDM)with the discrete fractional difference.Moreover,the chaos behaviors of the proposed map are observed and the bifurcation diagrams,the largest Lyapunov exponent plot,and the phase portraits are derived,respectively.Finally,with the secret keys generated by Menezes-Vanstone elliptic curve cryptosystem,we apply the discrete fractional map into color image encryption.After that,the image encryption algorithm is analyzed in four aspects and the result indicates that the proposed algorithm is more superior than the other algorithms.
文摘Objective: To research the clinical application of two-dimensional speckle tracking imaging (2D-STI) in quantitative assessment of left ventricular function in patients with obstructive sleep apnea-hypopnea syndrome (OSAS). Method: From July 2016 to December 2018, 86 patients with OSAS were selected as OSAS group. According to sleep apnea hypopnea index (AHI), they were divided into mild OSAS group (24 cases), moderate OSAS group (29 cases) and severe OSAS group (33 cases). Another 50 healthy volunteers who underwent physical examination in our hospital during the same period were selected as the control group. The left ventricular function of all patients was quantitatively assessed by 2D-STI. The left ventricular function of all patients was quantitatively assessed by 2D-STI. The results of routine echocardiography and left ventricular global strain parameters of the OSAS group, the control group and the OSAS patients with different severity were compared and analyzed. Result: There were no significant differences in the levels of left ventricular ejection fraction (LVEF), left ventricular end diastolic diameter (LVEDd), left ventricular end systolic diameter (LVESd) between the two groups and OSAS patients with different severity (P>0.05). The levels of IVST, LVPW and LVMI in the OSAS group were significantly higher than those in the control group, the levels of end-diastolic interventricular septal thickness (IVS), left ventricular posterior wall thickness (LVPW), left ventricular mass index (LVMI) in the severe OSAS group were significantly higher than those in the mild and moderate OSAS group, and the levels of IVST, LVPW and LVMI in the moderate OSAS group were significantly higher than those in the mild OSAS group, there were significant differences between groups (P<0.05). The levels of GLS, GRS and GCS in the OSAS group were significantly lower than those in the control group (P<0.05). GLS, GRS and GCS levels in the severe OSAS group were significantly lower than those in the mild OSAS group and the moderate OSAS group, while the levels of global longitudinal strain (GLS), global radial strain (GRS) and global circumferential strain (GCS) in the moderate OSAS group were significantly lower than those in the mild OSAS group (P<0.05). Conclusion: The left ventricular systolic function of OSAS patients is obviously impaired. Left ventricular function in OSAS patients can be assessed timely and accurately by two-dimensional speckle tracking imaging.
