Background With an aggravated social ageing level, the number of patients with Alzheimer's disease (AD) is gradually increasing, and mild cognitive impairment (MCI) is considered to be an early form of Alzheimer&#...Background With an aggravated social ageing level, the number of patients with Alzheimer's disease (AD) is gradually increasing, and mild cognitive impairment (MCI) is considered to be an early form of Alzheimer's disease. How to distinguish diseases in the early stage for the purposes of early diagnosis and treatment is an important topic. Aims The purpose of our study was to investigate the differences in brain cortical thickness and surface area among elderly patients with AD, elderly patients with amnestic MCI (aMCI) and normal controls (NC). Methods 20 AD patients, 21 aMCIs and 25 NC were recruited in the study. FreeSurfer software was used to calculate cortical thickness and surface area among groups. Results The patients with AD had less cortical thickness both in the left and right hemisphere in 17 of the 36 brain regions examined than the patients with aMCI or NC. The patients with AD also had smaller cerebral surface area both in the left and right hemisphere in 3 of the 36 brain regions examined than the patients with aMCI or NC. Compared with the NC, the patients with aMCI only had slight atrophy in the inferior parietal lobe of the left hemisphere, and no significant difference was found. Conclusion AD, as well as aMCI (to a lesser extent), is associated with reduced cortical thickness and surface area in a few brain regions associated with cognitive impairment. These results suggest that cortical thickness and surface area could be used for early detection of AD.展开更多
Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases rema...Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 termborn infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children's Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children's Hospital(approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR1800016409) and the protocol version is 1.0.展开更多
Deep learning techniques have been applied to the detection of gravitational wave signals in the past few years.Most existing methods focus on the data obtained by a single detector.However,the signal-to-noise ratio(S...Deep learning techniques have been applied to the detection of gravitational wave signals in the past few years.Most existing methods focus on the data obtained by a single detector.However,the signal-to-noise ratio(SNR)of gravitational wave signals in a single detector is pretty low,making it hard for deep neural networks to learn effective features.Therefore,how to use the observation signals obtained by multiple detectors in deep learning methods is a serious issue.We simulate binary neutron star signals from multiple detectors,including the Advanced LIGO and Virgo detectors.We calculate coherent SNR of multiple detectors using a fully coherent allsky search method and obtain the coherent SNR data required for our proposed deep learning method.Inspired by the principle of attention network Squeeze-and-Excitation Networks(SENet)and the soft thresholding shrinkage function,we propose a novel Squeeze-and-Excitation Shrinkage(SES)module to better extract effective features.Then we use this module to establish a gravitational wave squeeze-and-excitation shrinkage network(GWSESNet)detection model.We train and validate the performance of our model on the coherent SNR data set.Our model obtains satisfactory classification accuracy and can excellently complete the task of gravitational wave detection.展开更多
The traditional Range Doppler(RD)algorithm is unable to meet practical needs owing to the limit of resolution.The order of fractional Fourier Transform(FrFT)and the length of sampling signals affect SAR imaging perfor...The traditional Range Doppler(RD)algorithm is unable to meet practical needs owing to the limit of resolution.The order of fractional Fourier Transform(FrFT)and the length of sampling signals affect SAR imaging performance when FrFT is applied to RD algorithm.To overcome the above shortcomings,the purpose of this paper is to propose a high-resolution SAR image algorithm by using the optimal order of FrFT and the sample length constraints for the range direction.The expression of the optimal order of SAR range signals via FrFT is deduced in detail.The initial sample length and its constraints are proposed to obtain the best sample length of SAR range signals.Experimental results demonstrate that,when the range sampling-length changes in a certain interval,the best sampling-length will be obtained,which the best values of the range resolution,PSLR and ISLR,will be derived respectively.Compared with traditional RD algorithm,the main-lobe width of the peak-point target of the proposed algorithm is narrow in the range direction.While the peak amplitude of the first side-lobe is reduced significantly,those of other side-lobes also drop in various degrees.展开更多
A new information hiding technology named coverless information hiding is proposed.It uses original natural images as stego images to represent secret information.The focus of coverless image steganography method is h...A new information hiding technology named coverless information hiding is proposed.It uses original natural images as stego images to represent secret information.The focus of coverless image steganography method is how to represent image features and establish a map relationship between image feature and the secret information.In this paper,we use three kinds of features which are Local Binary Pattern(LBP),the mean value of pixels and the variance value of pixels.On this basis,we realize the transmission of secret information.Firstly,the hash sequence of the original cover image is obtained according to the description of the feature,and then the sequence of the secret information and the hash sequence of the original cover image are matched one by one.If the values are not the same,the image blocks of the original cover image are replaced according to the secret information to get the stego image.This paper explores the effect of three features on the visual quality of stego image.Experimental results show that the feature LBP is the best.展开更多
In this paper, we construct several efficient first-order splitting algorithms for solving a multi-block composite convex optimization problem. The objective function includes a smooth function with a Lipschitz contin...In this paper, we construct several efficient first-order splitting algorithms for solving a multi-block composite convex optimization problem. The objective function includes a smooth function with a Lipschitz continuous gradient, a proximable convex function that may be nonsmooth, and a finite sum composed of a proximable function and a bounded linear operator. To solve such an optimization problem, we transform it into the sum of three convex functions by defining an appropriate inner product space. Based on the dual forward-backward splitting algorithm and the primal-dual forward-backward splitting algorithm, we develop several iterative algorithms that involve only computing the gradient of the differentiable function and proximity operators of related convex functions. These iterative algorithms are matrix-inversion-free and completely splitting algorithms. Finally, we employ the proposed iterative algorithms to solve a regularized general prior image constrained compressed sensing model that is derived from computed tomography image reconstruction. Numerical results show that the proposed iterative algorithms outperform the compared algorithms including the alternating direction method of multipliers, the splitting primal-dual proximity algorithm, and the preconditioned splitting primal-dual proximity algorithm.展开更多
Longitudinal image analysis plays an important role in depicting the development of the brain structure,where image regression and interpolation are two commonly used techniques.In this paper,we develop an efficient m...Longitudinal image analysis plays an important role in depicting the development of the brain structure,where image regression and interpolation are two commonly used techniques.In this paper,we develop an efficient model and approach based on a path regression on the image manifold instead of the geodesic regression to avoid the complexity of the geodesic computation.Concretely,first we model the deformation by diffeomorphism;then,a large deformation is represented by a path on the orbit of the diffeomorphism group action.This path is obtained by compositing several small deformations,which can be well approximated by its linearization.Second,we introduce some intermediate images as constraints to the model,which guides to form the best-fitting path.Thirdly,we propose an approximated quadratic model by local linearization method,where a closed form is deduced for the solution.It actually speeds up the algorithm.Finally,we evaluate the proposed model and algorithm on a synthetic data and a real longitudinal MRI data.The results show that our proposed method outperforms several state-of-the-art methods.展开更多
基金Collaborative Innovation Center for Translational Medicine at Shanghai Jiao Tong University School of Medicine TM201728National Nature Science Foundation of China 81571298+2 种基金Shanghai health system excellent talent training program (excellent subject leader) project 2017BR054Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support 20172029Shanghai Pujiang Program 17PJD038.
文摘Background With an aggravated social ageing level, the number of patients with Alzheimer's disease (AD) is gradually increasing, and mild cognitive impairment (MCI) is considered to be an early form of Alzheimer's disease. How to distinguish diseases in the early stage for the purposes of early diagnosis and treatment is an important topic. Aims The purpose of our study was to investigate the differences in brain cortical thickness and surface area among elderly patients with AD, elderly patients with amnestic MCI (aMCI) and normal controls (NC). Methods 20 AD patients, 21 aMCIs and 25 NC were recruited in the study. FreeSurfer software was used to calculate cortical thickness and surface area among groups. Results The patients with AD had less cortical thickness both in the left and right hemisphere in 17 of the 36 brain regions examined than the patients with aMCI or NC. The patients with AD also had smaller cerebral surface area both in the left and right hemisphere in 3 of the 36 brain regions examined than the patients with aMCI or NC. Compared with the NC, the patients with aMCI only had slight atrophy in the inferior parietal lobe of the left hemisphere, and no significant difference was found. Conclusion AD, as well as aMCI (to a lesser extent), is associated with reduced cortical thickness and surface area in a few brain regions associated with cognitive impairment. These results suggest that cortical thickness and surface area could be used for early detection of AD.
基金supported by the Jiangsu Maternal and Child Health Research Project of China,No.F201612(to HXL)Changzhou Science and Technology Support Plan of China,No.CE20165027(to HXL)+1 种基金Changzhou City Planning Commission Major Science and Technology Projects of China,No.ZD201515(to HXL)Changzhou High Level Training Fund for Health Professionals of China,No.2016CZBJ028(to HXL)
文摘Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 termborn infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children's Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children's Hospital(approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR1800016409) and the protocol version is 1.0.
基金supported by the National Natural Science Foundation of China and Beijing Natural Science Foundation(No.4224091)China Postdoctoral Science Foundation(No.2021M693402)。
文摘Deep learning techniques have been applied to the detection of gravitational wave signals in the past few years.Most existing methods focus on the data obtained by a single detector.However,the signal-to-noise ratio(SNR)of gravitational wave signals in a single detector is pretty low,making it hard for deep neural networks to learn effective features.Therefore,how to use the observation signals obtained by multiple detectors in deep learning methods is a serious issue.We simulate binary neutron star signals from multiple detectors,including the Advanced LIGO and Virgo detectors.We calculate coherent SNR of multiple detectors using a fully coherent allsky search method and obtain the coherent SNR data required for our proposed deep learning method.Inspired by the principle of attention network Squeeze-and-Excitation Networks(SENet)and the soft thresholding shrinkage function,we propose a novel Squeeze-and-Excitation Shrinkage(SES)module to better extract effective features.Then we use this module to establish a gravitational wave squeeze-and-excitation shrinkage network(GWSESNet)detection model.We train and validate the performance of our model on the coherent SNR data set.Our model obtains satisfactory classification accuracy and can excellently complete the task of gravitational wave detection.
基金This work is supported by the 13th Five-Year Plan for Jiangsu Education Science(D/2020/01/22)JSPIGKZ and Natural Science Research Projects of Colleges and Universities in Jiangsu Province(19KJB510022)。
文摘The traditional Range Doppler(RD)algorithm is unable to meet practical needs owing to the limit of resolution.The order of fractional Fourier Transform(FrFT)and the length of sampling signals affect SAR imaging performance when FrFT is applied to RD algorithm.To overcome the above shortcomings,the purpose of this paper is to propose a high-resolution SAR image algorithm by using the optimal order of FrFT and the sample length constraints for the range direction.The expression of the optimal order of SAR range signals via FrFT is deduced in detail.The initial sample length and its constraints are proposed to obtain the best sample length of SAR range signals.Experimental results demonstrate that,when the range sampling-length changes in a certain interval,the best sampling-length will be obtained,which the best values of the range resolution,PSLR and ISLR,will be derived respectively.Compared with traditional RD algorithm,the main-lobe width of the peak-point target of the proposed algorithm is narrow in the range direction.While the peak amplitude of the first side-lobe is reduced significantly,those of other side-lobes also drop in various degrees.
基金This work is supported by the National Key R&D Program of China under grant 2018YFB1003205by the National Natural Science Foundation of China under grant U1836208,U1536206,U1836110,61602253,61672294+2 种基金by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20181407by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘A new information hiding technology named coverless information hiding is proposed.It uses original natural images as stego images to represent secret information.The focus of coverless image steganography method is how to represent image features and establish a map relationship between image feature and the secret information.In this paper,we use three kinds of features which are Local Binary Pattern(LBP),the mean value of pixels and the variance value of pixels.On this basis,we realize the transmission of secret information.Firstly,the hash sequence of the original cover image is obtained according to the description of the feature,and then the sequence of the secret information and the hash sequence of the original cover image are matched one by one.If the values are not the same,the image blocks of the original cover image are replaced according to the secret information to get the stego image.This paper explores the effect of three features on the visual quality of stego image.Experimental results show that the feature LBP is the best.
基金The authors would like to thank the two anonymous reviewers for their suggestions and comments to improve the manuscript. This work was supported by the National Natural Science Foundations of China (11401293, 11661056, 11771198)the Natural Science Foundations of Jiangxi Province (20151BAB211010)+1 种基金the China Postdoctoral Science Foundation (2015M571989)the Jiangxi Province Postdoctoral Science Foundation (2015KY51).
文摘In this paper, we construct several efficient first-order splitting algorithms for solving a multi-block composite convex optimization problem. The objective function includes a smooth function with a Lipschitz continuous gradient, a proximable convex function that may be nonsmooth, and a finite sum composed of a proximable function and a bounded linear operator. To solve such an optimization problem, we transform it into the sum of three convex functions by defining an appropriate inner product space. Based on the dual forward-backward splitting algorithm and the primal-dual forward-backward splitting algorithm, we develop several iterative algorithms that involve only computing the gradient of the differentiable function and proximity operators of related convex functions. These iterative algorithms are matrix-inversion-free and completely splitting algorithms. Finally, we employ the proposed iterative algorithms to solve a regularized general prior image constrained compressed sensing model that is derived from computed tomography image reconstruction. Numerical results show that the proposed iterative algorithms outperform the compared algorithms including the alternating direction method of multipliers, the splitting primal-dual proximity algorithm, and the preconditioned splitting primal-dual proximity algorithm.
基金The research was supported by the National Natural Science Foundation of China(Nos.11771276,11471208)the Capacity Construction Project of Local Universities in Shanghai(No.18010500600).
文摘Longitudinal image analysis plays an important role in depicting the development of the brain structure,where image regression and interpolation are two commonly used techniques.In this paper,we develop an efficient model and approach based on a path regression on the image manifold instead of the geodesic regression to avoid the complexity of the geodesic computation.Concretely,first we model the deformation by diffeomorphism;then,a large deformation is represented by a path on the orbit of the diffeomorphism group action.This path is obtained by compositing several small deformations,which can be well approximated by its linearization.Second,we introduce some intermediate images as constraints to the model,which guides to form the best-fitting path.Thirdly,we propose an approximated quadratic model by local linearization method,where a closed form is deduced for the solution.It actually speeds up the algorithm.Finally,we evaluate the proposed model and algorithm on a synthetic data and a real longitudinal MRI data.The results show that our proposed method outperforms several state-of-the-art methods.
