Computed tomography has made significant advances since its intro-duction in the early 1970s,where researchers have mainly focused on the quality of image reconstruction in the early stage.However,radiation exposure p...Computed tomography has made significant advances since its intro-duction in the early 1970s,where researchers have mainly focused on the quality of image reconstruction in the early stage.However,radiation exposure poses a health risk,prompting the demand of the lowest possible dose when carrying out CT examinations.To acquire high-quality reconstruction images with low dose radiation,CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction,to reconstruction methods based on artificial intelligence(AI).All these efforts are devoted to con-structing high-quality images using only low doses with fast reconstruction speed.In particular,conventional reconstruction methods usually optimize one aspect,while AI-based reconstruction has finally managed to attain all goals in one shot.However,there are limitations such as the requirements on large datasets,unstable performance,and weak generalizability in AI-based reconstruction methods.This work presents the review and discussion on the classification,the commercial use,the advantages,and the limitations of AI-based image reconstruction methods in CT.展开更多
In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reco...In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reconstructed by traditional associative imaging methods. Unlike traditional ghost imaging to reconstruct objects from bucket signals, our proposed method can use simple objects (such as EMNIST) as a training set for GAN, and then recognize objects (such as faces) of completely different complexity than the training set. We use traditional ghost imaging and neural network to reconstruct target objects respectively. According to the research results in this paper, the method based on neural network can reconstruct complex objects very well, but the method based on traditional ghost imaging cannot reconstruct complex objects. The research scheme in this paper is of great significance for the reconstruction of complex object-related imaging under low sampling conditions.展开更多
Traditional three-dimensional(3D)image reconstruction method,which highly dependent on the environment and has poor reconstruction effect,is easy to lead to mismatch and poor real-time performance.The accuracy of feat...Traditional three-dimensional(3D)image reconstruction method,which highly dependent on the environment and has poor reconstruction effect,is easy to lead to mismatch and poor real-time performance.The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology.To solve the problem,a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper.The algorithm first extracts the feature information of multiple two-dimensional(2D)images based on scale and rotation invariance parameters of Scale-invariant feature transform(SIFT)operator.Secondly,self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability.Then,Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures,and Generative Adversarial Networks(GAN)loss function is used to replace mean square error(MSE)to better express image features,discarding useless features to obtain effective image features.Finally,an incremental structure from motion(SFM)algorithm is performed to calculate rotation matrix and translation vector of the camera,and the feature points are triangulated to obtain a sparse spatial point cloud,and meshlab software is used to display the results.Simulation experiments show that compared with the traditional method,the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud,with an accuracy rate of 92.5%and a reconstruction complete rate of 83.6%.展开更多
A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper,which consists of three steps to avoid the computational complexity: a single image SR re...A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper,which consists of three steps to avoid the computational complexity: a single image SR reconstruction step,a registration step and a wavelet-based image fusion. This algorithm decomposes two large matrixes to the tensor product of two little matrixes and uses the natural isomorphism between matrix space and vector space to transform cost function based on matrix-vector products model to matrix form. Furthermore,we prove that the regularization part can be transformed to the matrix formed. The conjugate-gradient method is used to solve this new model. Finally,the wavelet fusion is used to integrate all the registered highresolution images obtained from the single image SR reconstruction step. The proposed algorithm reduces the storage requirement and the calculating complexity,and can be applied to large-dimension low-resolution images.展开更多
The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based.They are iterative algorithms that optimize objective functions with spatial and/or temporal const...The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based.They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints.This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm.The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study.The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.展开更多
Electrical Impedance Tomography (EIT) is a medical imaging technique which can be used to monitor the regional ventilation in patients utilizing voltage measurements made at the thorax. Several reconstruction algorith...Electrical Impedance Tomography (EIT) is a medical imaging technique which can be used to monitor the regional ventilation in patients utilizing voltage measurements made at the thorax. Several reconstruction algorithms have been developed during the last few years. In this manuscript we compare a well-established algorithm and a re-cently developed method for image reconstruction regarding EIT indices derived from the differently reconstructed images.展开更多
In this editorial,we review recent progress on the applications of artificial intelligence(AI)in image reconstruction for biomedical imaging.Because it abandons prior information of traditional artificial design and a...In this editorial,we review recent progress on the applications of artificial intelligence(AI)in image reconstruction for biomedical imaging.Because it abandons prior information of traditional artificial design and adopts a completely data-driven mode to obtain deeper prior information via learning,AI technology plays an increasingly important role in biomedical image reconstruction.The combination of AI technology and the biomedical image reconstruction method has become a hotspot in the field.Favoring AI,the performance of biomedical image reconstruction has been improved in terms of accuracy,resolution,imaging speed,etc.We specifically focus on how to use AI technology to improve the performance of biomedical image reconstruction,and propose possible future directions in this field.展开更多
Purpose Accurate localization of radioactive materials is critical to nuclear safety and nuclear security.A coded aperture imaging system provides a visualization solution.However,the correlation method has poor recon...Purpose Accurate localization of radioactive materials is critical to nuclear safety and nuclear security.A coded aperture imaging system provides a visualization solution.However,the correlation method has poor reconstruction performance for sources with low counts and for extended sources.Methods In this study,a Monte Carlo optimization-based MLEM algorithm(MC-MLEM)is proposed.The system matrix was obtained by accurate Monte Carlo simulation,so the physical effects such as mask penetration that affect the imaging process were taken into account in the MLEM algorithm.In the simulation process,the normalization of the system matrix was realized by controlling the source at different position of the source plane to have the same activity and emission angle.Results The experimental results showed that compared with the correlation method,the MC-MLEM algorithm could improve the signal-to-noise ratio and angular resolution and locate the source position quickly and accurately under low count conditions.Furthermore,the MC-MLEM algorithm could reconstruct the shape of the extended source and the expected activity ratio of cold-hot sources with large activity differences.Conclusion The MC-MLEM algorithm improved the imaging results and enhanced the reconstruction performance.展开更多
Compressed sensing(CS)has been successfully applied to realize image reconstruction.Neural networks have been introduced to the CS of images to exploit the prior known support information,which can improve the reconst...Compressed sensing(CS)has been successfully applied to realize image reconstruction.Neural networks have been introduced to the CS of images to exploit the prior known support information,which can improve the reconstruction quality.Capsule Network(Caps Net)is the latest achievement in neural networks,and can well represent the instantiation parameters of a specific type of entity or part of an object.This study aims to propose a Caps Net with a novel dynamic routing to embed the information within the CS framework.The output of the network represents the probability that the index of the nonzero entry exists on the support of the signal of interest.To lead the dynamic routing to the most likely index,a group of prediction vectors is designed determined by the information.Furthermore,the results of experiments on imaging signals are taken for a comparation of the performances among different algorithms.It is concluded that the proposed capsule network(Caps Net)creates higher reconstruction quality at nearly the same time with traditional Caps Net.展开更多
Super-resolution structured illumination microscopy(SR-SIM)is an outstanding method for visualizing the subcellular dynamics in living cells.To date,by using elaborately designed systems and algorithms,SR-SIM can achi...Super-resolution structured illumination microscopy(SR-SIM)is an outstanding method for visualizing the subcellular dynamics in living cells.To date,by using elaborately designed systems and algorithms,SR-SIM can achieve rapid,optically sectioned,SR observation with hundreds to thousands of time points.However,real-time observation is still out of reach for most SIM setups as conventional algorithms for image reconstruction involve a heavy computing burden.To address this limitation,an accelerated reconstruction algorithm was developed by implementing a simplified workflow for SR-SIM,termed joint space and frequency reconstruction.This algorithm results in an 80-fold improvement in reconstruction speed relative to the widely used Wiener-SIM.Critically,the increased processing speed does not come at the expense of spatial resolution or sectioning capability,as demonstrated by live imaging of microtubule dynamics and mitochondrial tubulation.展开更多
Electrical capacitance tomography(ECT)has been applied to two-phase flow measurement in recent years.Image reconstruction algorithms play an important role in the successful applications of ECT.To solve the ill-posed ...Electrical capacitance tomography(ECT)has been applied to two-phase flow measurement in recent years.Image reconstruction algorithms play an important role in the successful applications of ECT.To solve the ill-posed and nonlinear inverse problem of ECT image reconstruction,a new ECT image reconstruction method based on fast linearized alternating direction method of multipliers(FLADMM)is proposed in this paper.On the basis of theoretical analysis of compressed sensing(CS),the data acquisition of ECT is regarded as a linear measurement process of permittivity distribution signal of pipe section.A new measurement matrix is designed and L1 regularization method is used to convert ECT inverse problem to a convex relaxation problem which contains prior knowledge.A new fast alternating direction method of multipliers which contained linearized idea is employed to minimize the objective function.Simulation data and experimental results indicate that compared with other methods,the quality and speed of reconstructed images are markedly improved.Also,the dynamic experimental results indicate that the proposed algorithm can ful fill the real-time requirement of ECT systems in the application.展开更多
In image restoration,we usually assume that the underlying image has a good sparse approximation under a certain system.Wavelet tight frame system has been proven to be such an efficient system to sparsely approximate...In image restoration,we usually assume that the underlying image has a good sparse approximation under a certain system.Wavelet tight frame system has been proven to be such an efficient system to sparsely approximate piecewise smooth images.Thus,it has been widely used in many practical image restoration problems.However,images from different scenarios are so diverse that no static wavelet tight frame system can sparsely approximate all of themwell.To overcome this,recently,Cai et.al.(Appl Comput Harmon Anal 37:89–105,2014)proposed a method that derives a data-driven tight frame adapted to the specific input image,leading to a better sparse approximation.The data-driven tight frame has been applied successfully to image denoising and CT image reconstruction.In this paper,we extend this data-driven tight frame construction method to multi-channel images.We construct a discrete tight frame system for each channel and assume their sparse coefficients have a joint sparsity.The multi-channel data-driven tight frame construction scheme is applied to joint color and depth image reconstruction.Experimental results show that the proposed approach has a better performance than state-of-the-art joint color and depth image reconstruction approaches.展开更多
The Computer Tomography(CT)method is used for remote sensing the Earth’s plasmasphere.One challenge for image reconstruction is insufficient projection data,mainly caused by limited projection angles.In this study,we...The Computer Tomography(CT)method is used for remote sensing the Earth’s plasmasphere.One challenge for image reconstruction is insufficient projection data,mainly caused by limited projection angles.In this study,we apply the Algebraic Reconstruction Technique(ART)and the minimization of the image Total Variation(TV)method,with a combination of priori knowledge of north–south symmetry,to reconstruct plasmaspheric He+density from simulated EUV images.The results demonstrate that incorporating priori assumption can be particularly useful when the projection data is insufficient.This method has good performance even with a projection angle of less than 150 degrees.The method of our study is expected to have applications in the Soft X-ray Imager(SXI)reconstruction for the Solar wind–Magnetosphere–Ionosphere Link Explorer(SMILE)mission.展开更多
Electrical impedance tomography(EIT)image reconstruction is a non-linear problem.In general,finite element model is the critical basis of EIT image reconstruction.A 3D human thorax modeling method for EIT image recons...Electrical impedance tomography(EIT)image reconstruction is a non-linear problem.In general,finite element model is the critical basis of EIT image reconstruction.A 3D human thorax modeling method for EIT image reconstruction is proposed herein to improve the accuracy and reduce the complexity of existing finite element modeling methods.The contours of human thorax and lungs are extracted from the layers of magnetic resonance imaging(MRI)images by an optimized Otsu’s method for the construction of the 3D human thorax model including the lung models.Furthermore,the GMSH tool is used for finite element subdivision to generate the 3D finite element model of human thorax.The proposed modeling method is fast and accurate,and it is universal for different types of MRI images.The effectiveness of the proposed method is validated by extensive numerical simulation in MATLAB.The results show that the individually oriented 3D finite element model can improve the reconstruction quality of the EIT images more effectively than the cylindrical model,the 2.5D model and other human chest models.展开更多
Using a CT scan of the pulmonary tissue,a human pulmonary model is established combined with the structure property of the human lung tissue using the software COMSOL.Combined with the conductivity contribution inform...Using a CT scan of the pulmonary tissue,a human pulmonary model is established combined with the structure property of the human lung tissue using the software COMSOL.Combined with the conductivity contribution information of the human tissue and organ,an image reconstruction method of electrical impedance tomography based on pulmonary prior information is proposed using the conjugate gradient method.Simulation results show that the uniformity index of sensitivity distribution of the pulmonary model is 15.568,which is significantly reduced compared with 34.218 based on the round field.The proposed algorithm improves the uniformity of the sensing field,the image resolution of the conductivity distribution of pulmonary tissue and the quality of the reconstruction image based on pulmonary prior information.展开更多
Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients.Deep learning and its applications in medical imaging,especially in image reconstruc...Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients.Deep learning and its applications in medical imaging,especially in image reconstruction have received considerable attention in the literature in recent years.This study reviews records obtained elec-tronically through the leading scientific databases(Magnetic Resonance Imaging journal,Google Scholar,Scopus,Science Direct,Elsevier,and from other journal publications)searched using three sets of keywords:(1)Deep learning,image reconstruction,medical imaging;(2)Medical imaging,Deep learning,Image reconstruction;(3)Open science,Open imaging data,Open software.The articles reviewed revealed that deep learning-based re-construction methods improve the quality of reconstructed images qualitatively and quantitatively.However,deep learning techniques are generally computationally expensive,require large amounts of training datasets,lack decent theory to explain why the algorithms work,and have issues of generalization and robustness.The challenge of lack of enough training datasets is currently being addressed by using transfer learning techniques.展开更多
基金This work is supported by the National Key Research and Development Program of China(2020YFC2003400)Qiang Ni’s work was funded by the UK EPSRC project under grant number EP/K011693/1.
文摘Computed tomography has made significant advances since its intro-duction in the early 1970s,where researchers have mainly focused on the quality of image reconstruction in the early stage.However,radiation exposure poses a health risk,prompting the demand of the lowest possible dose when carrying out CT examinations.To acquire high-quality reconstruction images with low dose radiation,CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction,to reconstruction methods based on artificial intelligence(AI).All these efforts are devoted to con-structing high-quality images using only low doses with fast reconstruction speed.In particular,conventional reconstruction methods usually optimize one aspect,while AI-based reconstruction has finally managed to attain all goals in one shot.However,there are limitations such as the requirements on large datasets,unstable performance,and weak generalizability in AI-based reconstruction methods.This work presents the review and discussion on the classification,the commercial use,the advantages,and the limitations of AI-based image reconstruction methods in CT.
文摘In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reconstructed by traditional associative imaging methods. Unlike traditional ghost imaging to reconstruct objects from bucket signals, our proposed method can use simple objects (such as EMNIST) as a training set for GAN, and then recognize objects (such as faces) of completely different complexity than the training set. We use traditional ghost imaging and neural network to reconstruct target objects respectively. According to the research results in this paper, the method based on neural network can reconstruct complex objects very well, but the method based on traditional ghost imaging cannot reconstruct complex objects. The research scheme in this paper is of great significance for the reconstruction of complex object-related imaging under low sampling conditions.
基金This work is funded by Key Scientific Research Projects of Colleges and Universities in Henan Province under Grant 22A460022Training Plan for Young Backbone Teachers in Colleges and Universities in Henan Province under Grant 2021GGJS077.
文摘Traditional three-dimensional(3D)image reconstruction method,which highly dependent on the environment and has poor reconstruction effect,is easy to lead to mismatch and poor real-time performance.The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology.To solve the problem,a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper.The algorithm first extracts the feature information of multiple two-dimensional(2D)images based on scale and rotation invariance parameters of Scale-invariant feature transform(SIFT)operator.Secondly,self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability.Then,Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures,and Generative Adversarial Networks(GAN)loss function is used to replace mean square error(MSE)to better express image features,discarding useless features to obtain effective image features.Finally,an incremental structure from motion(SFM)algorithm is performed to calculate rotation matrix and translation vector of the camera,and the feature points are triangulated to obtain a sparse spatial point cloud,and meshlab software is used to display the results.Simulation experiments show that compared with the traditional method,the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud,with an accuracy rate of 92.5%and a reconstruction complete rate of 83.6%.
基金Sponsored by the National Natural Science Foundation of China(Grant No.60474016)the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.2009046)
文摘A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper,which consists of three steps to avoid the computational complexity: a single image SR reconstruction step,a registration step and a wavelet-based image fusion. This algorithm decomposes two large matrixes to the tensor product of two little matrixes and uses the natural isomorphism between matrix space and vector space to transform cost function based on matrix-vector products model to matrix form. Furthermore,we prove that the regularization part can be transformed to the matrix formed. The conjugate-gradient method is used to solve this new model. Finally,the wavelet fusion is used to integrate all the registered highresolution images obtained from the single image SR reconstruction step. The proposed algorithm reduces the storage requirement and the calculating complexity,and can be applied to large-dimension low-resolution images.
基金supported by American Heart Association,No.18AJML34280074.
文摘The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based.They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints.This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm.The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study.The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.
文摘Electrical Impedance Tomography (EIT) is a medical imaging technique which can be used to monitor the regional ventilation in patients utilizing voltage measurements made at the thorax. Several reconstruction algorithms have been developed during the last few years. In this manuscript we compare a well-established algorithm and a re-cently developed method for image reconstruction regarding EIT indices derived from the differently reconstructed images.
基金Supported by The National Key R&D Program of China,No.2018YFC0910600the National Natural Science Foundation of China No.81627807 and 11727813+2 种基金Shaanxi Science Funds for Distinguished Young Scholars,No.2020JC-27the Fok Ying Tung Education Foundation,No.161104and Program for the Young Topnotch Talent of Shaanxi Province.
文摘In this editorial,we review recent progress on the applications of artificial intelligence(AI)in image reconstruction for biomedical imaging.Because it abandons prior information of traditional artificial design and adopts a completely data-driven mode to obtain deeper prior information via learning,AI technology plays an increasingly important role in biomedical image reconstruction.The combination of AI technology and the biomedical image reconstruction method has become a hotspot in the field.Favoring AI,the performance of biomedical image reconstruction has been improved in terms of accuracy,resolution,imaging speed,etc.We specifically focus on how to use AI technology to improve the performance of biomedical image reconstruction,and propose possible future directions in this field.
基金supported by the National Natural Science Foundation of China(No.11905229).
文摘Purpose Accurate localization of radioactive materials is critical to nuclear safety and nuclear security.A coded aperture imaging system provides a visualization solution.However,the correlation method has poor reconstruction performance for sources with low counts and for extended sources.Methods In this study,a Monte Carlo optimization-based MLEM algorithm(MC-MLEM)is proposed.The system matrix was obtained by accurate Monte Carlo simulation,so the physical effects such as mask penetration that affect the imaging process were taken into account in the MLEM algorithm.In the simulation process,the normalization of the system matrix was realized by controlling the source at different position of the source plane to have the same activity and emission angle.Results The experimental results showed that compared with the correlation method,the MC-MLEM algorithm could improve the signal-to-noise ratio and angular resolution and locate the source position quickly and accurately under low count conditions.Furthermore,the MC-MLEM algorithm could reconstruct the shape of the extended source and the expected activity ratio of cold-hot sources with large activity differences.Conclusion The MC-MLEM algorithm improved the imaging results and enhanced the reconstruction performance.
基金supported by the Research Fund Project of Beijing Information Science and Technology University(2021XJJ44 and 2021XJJ69).
文摘Compressed sensing(CS)has been successfully applied to realize image reconstruction.Neural networks have been introduced to the CS of images to exploit the prior known support information,which can improve the reconstruction quality.Capsule Network(Caps Net)is the latest achievement in neural networks,and can well represent the instantiation parameters of a specific type of entity or part of an object.This study aims to propose a Caps Net with a novel dynamic routing to embed the information within the CS framework.The output of the network represents the probability that the index of the nonzero entry exists on the support of the signal of interest.To lead the dynamic routing to the most likely index,a group of prediction vectors is designed determined by the information.Furthermore,the results of experiments on imaging signals are taken for a comparation of the performances among different algorithms.It is concluded that the proposed capsule network(Caps Net)creates higher reconstruction quality at nearly the same time with traditional Caps Net.
基金supported by the National Natural Science Foundation of China (NSFC) (Nos. 62005208, 62135003, and 61905189)Innovation Capability Support Program of Shaanxi (No. 2021TD-57)+1 种基金China Postdoctoral Science Foundation (Nos. 2020M673365 and 2019M663656)National Institutes of Health Grant GM100156 to PRB
文摘Super-resolution structured illumination microscopy(SR-SIM)is an outstanding method for visualizing the subcellular dynamics in living cells.To date,by using elaborately designed systems and algorithms,SR-SIM can achieve rapid,optically sectioned,SR observation with hundreds to thousands of time points.However,real-time observation is still out of reach for most SIM setups as conventional algorithms for image reconstruction involve a heavy computing burden.To address this limitation,an accelerated reconstruction algorithm was developed by implementing a simplified workflow for SR-SIM,termed joint space and frequency reconstruction.This algorithm results in an 80-fold improvement in reconstruction speed relative to the widely used Wiener-SIM.Critically,the increased processing speed does not come at the expense of spatial resolution or sectioning capability,as demonstrated by live imaging of microtubule dynamics and mitochondrial tubulation.
基金Supported by the National Natural Science Foundation of China(61203021)the Key Science and Technology Program of Liaoning Province(2011216011)+1 种基金the Natural Science Foundation of Liaoning Province(2013020024)the Program for Liaoning Excellent Talents in Universities(LJQ2015061)
文摘Electrical capacitance tomography(ECT)has been applied to two-phase flow measurement in recent years.Image reconstruction algorithms play an important role in the successful applications of ECT.To solve the ill-posed and nonlinear inverse problem of ECT image reconstruction,a new ECT image reconstruction method based on fast linearized alternating direction method of multipliers(FLADMM)is proposed in this paper.On the basis of theoretical analysis of compressed sensing(CS),the data acquisition of ECT is regarded as a linear measurement process of permittivity distribution signal of pipe section.A new measurement matrix is designed and L1 regularization method is used to convert ECT inverse problem to a convex relaxation problem which contains prior knowledge.A new fast alternating direction method of multipliers which contained linearized idea is employed to minimize the objective function.Simulation data and experimental results indicate that compared with other methods,the quality and speed of reconstructed images are markedly improved.Also,the dynamic experimental results indicate that the proposed algorithm can ful fill the real-time requirement of ECT systems in the application.
基金Jian-Feng Cai is partially supported by the National Natural Science Foundation of USA(No.DMS 1418737).
文摘In image restoration,we usually assume that the underlying image has a good sparse approximation under a certain system.Wavelet tight frame system has been proven to be such an efficient system to sparsely approximate piecewise smooth images.Thus,it has been widely used in many practical image restoration problems.However,images from different scenarios are so diverse that no static wavelet tight frame system can sparsely approximate all of themwell.To overcome this,recently,Cai et.al.(Appl Comput Harmon Anal 37:89–105,2014)proposed a method that derives a data-driven tight frame adapted to the specific input image,leading to a better sparse approximation.The data-driven tight frame has been applied successfully to image denoising and CT image reconstruction.In this paper,we extend this data-driven tight frame construction method to multi-channel images.We construct a discrete tight frame system for each channel and assume their sparse coefficients have a joint sparsity.The multi-channel data-driven tight frame construction scheme is applied to joint color and depth image reconstruction.Experimental results show that the proposed approach has a better performance than state-of-the-art joint color and depth image reconstruction approaches.
基金supported by the National Natural Science Foundation of China(Grant Nos.41904148,41731070,41874175)in part by the Strategic Priority Program on Space Science,Chinese Academy of Sciences(Grant Nos.XDA15017000,XDA15350201,XDA15052500).
文摘The Computer Tomography(CT)method is used for remote sensing the Earth’s plasmasphere.One challenge for image reconstruction is insufficient projection data,mainly caused by limited projection angles.In this study,we apply the Algebraic Reconstruction Technique(ART)and the minimization of the image Total Variation(TV)method,with a combination of priori knowledge of north–south symmetry,to reconstruct plasmaspheric He+density from simulated EUV images.The results demonstrate that incorporating priori assumption can be particularly useful when the projection data is insufficient.This method has good performance even with a projection angle of less than 150 degrees.The method of our study is expected to have applications in the Soft X-ray Imager(SXI)reconstruction for the Solar wind–Magnetosphere–Ionosphere Link Explorer(SMILE)mission.
基金the National Natural Science Foundation of China(No.61371017)。
文摘Electrical impedance tomography(EIT)image reconstruction is a non-linear problem.In general,finite element model is the critical basis of EIT image reconstruction.A 3D human thorax modeling method for EIT image reconstruction is proposed herein to improve the accuracy and reduce the complexity of existing finite element modeling methods.The contours of human thorax and lungs are extracted from the layers of magnetic resonance imaging(MRI)images by an optimized Otsu’s method for the construction of the 3D human thorax model including the lung models.Furthermore,the GMSH tool is used for finite element subdivision to generate the 3D finite element model of human thorax.The proposed modeling method is fast and accurate,and it is universal for different types of MRI images.The effectiveness of the proposed method is validated by extensive numerical simulation in MATLAB.The results show that the individually oriented 3D finite element model can improve the reconstruction quality of the EIT images more effectively than the cylindrical model,the 2.5D model and other human chest models.
基金supported by the National Key Technology R&D Program (Grant No.2006BAIO3A00)the Natural Science Foundation of Tianjin Municipal Science and Technology Commission (No.08JCYBJC03500).
文摘Using a CT scan of the pulmonary tissue,a human pulmonary model is established combined with the structure property of the human lung tissue using the software COMSOL.Combined with the conductivity contribution information of the human tissue and organ,an image reconstruction method of electrical impedance tomography based on pulmonary prior information is proposed using the conjugate gradient method.Simulation results show that the uniformity index of sensitivity distribution of the pulmonary model is 15.568,which is significantly reduced compared with 34.218 based on the round field.The proposed algorithm improves the uniformity of the sensing field,the image resolution of the conductivity distribution of pulmonary tissue and the quality of the reconstruction image based on pulmonary prior information.
基金This research was made possible through the Dutch organization NWO-WOTRO(Grants No.W 07.303.101:‘A sustainable MRI system to diagnose hydrocephalus in Uganda’).
文摘Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients.Deep learning and its applications in medical imaging,especially in image reconstruction have received considerable attention in the literature in recent years.This study reviews records obtained elec-tronically through the leading scientific databases(Magnetic Resonance Imaging journal,Google Scholar,Scopus,Science Direct,Elsevier,and from other journal publications)searched using three sets of keywords:(1)Deep learning,image reconstruction,medical imaging;(2)Medical imaging,Deep learning,Image reconstruction;(3)Open science,Open imaging data,Open software.The articles reviewed revealed that deep learning-based re-construction methods improve the quality of reconstructed images qualitatively and quantitatively.However,deep learning techniques are generally computationally expensive,require large amounts of training datasets,lack decent theory to explain why the algorithms work,and have issues of generalization and robustness.The challenge of lack of enough training datasets is currently being addressed by using transfer learning techniques.
