The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterat...The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterative computed tomographic reconstruction.The distance-driven model（DDM） is a state-of-the-art technology that simulates forward and back projections.This model has a low computational complexity and a relatively high spatial resolution;however,it includes only a few methods in a parallel operation with a matched model scheme.This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations.Our proposed model has been implemented on a GPU（graphic processing unit） platform and has achieved satisfactory computational efficiency with no approximation.The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop,respectively,with an image size of 256×256×256 and 360 projections with a size of 512×512.We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation.The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.展开更多
It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used suc...It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.展开更多
The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potentia...The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.展开更多
针对高级量测体系中的海量数据问题,首次引入压缩感知以克服传统数据压缩方法的不足,深入探索了基于压缩感知的高级量测体系(advanced metering infrastructure based on compressed sensing,AMI-CS)。首先,在分析各类数据特点的基础上...针对高级量测体系中的海量数据问题,首次引入压缩感知以克服传统数据压缩方法的不足,深入探索了基于压缩感知的高级量测体系(advanced metering infrastructure based on compressed sensing,AMI-CS)。首先,在分析各类数据特点的基础上,提出了基于时间和基于空间的2种基本模型及其选取原则;然后,设计模型中的关键要素,提出分类K-SVD稀疏基和适用于时间模型的优选重构算法,并设置二进稀疏测量方式、通用重构算法及适用采集参数;基于此,形成了AMI-CS具体构建方案。实验结果表明,所提出的AMI-CS方案关键要素均具合理性,优于CS传统要素且较传统压缩提升了抗丢包性,通过合理选择压缩比,数据重构信噪比在58 dB以上、重构误差在0.24%以下,满足AMI要求。展开更多
目的:研究西门子多排螺旋CT的高级建模迭代重建(ADMIRE)算法对重建图像质量的影响。方法:在同一扫描条件下,使用西门子SOMATOM Force CT对Catphan500体模的CTP515和CTP528模块进行螺旋扫描,未用或使用不同强度的ADMIRE算法对图像进行重...目的:研究西门子多排螺旋CT的高级建模迭代重建(ADMIRE)算法对重建图像质量的影响。方法:在同一扫描条件下,使用西门子SOMATOM Force CT对Catphan500体模的CTP515和CTP528模块进行螺旋扫描,未用或使用不同强度的ADMIRE算法对图像进行重建,对不同重建条件下的图像进行主观和客观评价。结果:当图像低对比度为0.3%、0.5%和1.0%时,未用ADMIRE对图像进行重建,其噪声分别为4.90、4.50和5.23;使用ADMIRE2和ADMIRE5重建图像时其噪声分别减少18.35%和53.23%、18.82%和52.32%以及20.33%和53.79%。用ADMIRE0、ADMIRE2和ADMIRE5分别对CTP528模块图像进行重建,对其图像进行主观观察,可观察到的最小细节相同,均为7.0 LP/cm。结论:使用ADMIRE对图像进行重建可以在不影响图像清晰度的同时有效降低噪声,提高CT图像的低对比度分辨率,以帮助临床医师更好的观察组织密度相近的病灶,为临床医师诊断病情提供帮助。展开更多
基金supported by the National High Technology Research and Development Program of China(Grant No.2012AA011603)the National Natural Science Foundation of China(Grant No.61372172)
文摘The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterative computed tomographic reconstruction.The distance-driven model（DDM） is a state-of-the-art technology that simulates forward and back projections.This model has a low computational complexity and a relatively high spatial resolution;however,it includes only a few methods in a parallel operation with a matched model scheme.This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations.Our proposed model has been implemented on a GPU（graphic processing unit） platform and has achieved satisfactory computational efficiency with no approximation.The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop,respectively,with an image size of 256×256×256 and 360 projections with a size of 512×512.We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation.The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.
文摘It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.
基金Project(61171133)supported by the National Natural Science Foundation of ChinaProject(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProject(61101182)supported by National Natural Science Foundation for Young Scientists of China
文摘The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.
文摘针对高级量测体系中的海量数据问题,首次引入压缩感知以克服传统数据压缩方法的不足,深入探索了基于压缩感知的高级量测体系(advanced metering infrastructure based on compressed sensing,AMI-CS)。首先,在分析各类数据特点的基础上,提出了基于时间和基于空间的2种基本模型及其选取原则;然后,设计模型中的关键要素,提出分类K-SVD稀疏基和适用于时间模型的优选重构算法,并设置二进稀疏测量方式、通用重构算法及适用采集参数;基于此,形成了AMI-CS具体构建方案。实验结果表明,所提出的AMI-CS方案关键要素均具合理性,优于CS传统要素且较传统压缩提升了抗丢包性,通过合理选择压缩比,数据重构信噪比在58 dB以上、重构误差在0.24%以下,满足AMI要求。
文摘目的:研究西门子多排螺旋CT的高级建模迭代重建(ADMIRE)算法对重建图像质量的影响。方法:在同一扫描条件下,使用西门子SOMATOM Force CT对Catphan500体模的CTP515和CTP528模块进行螺旋扫描,未用或使用不同强度的ADMIRE算法对图像进行重建,对不同重建条件下的图像进行主观和客观评价。结果:当图像低对比度为0.3%、0.5%和1.0%时,未用ADMIRE对图像进行重建,其噪声分别为4.90、4.50和5.23;使用ADMIRE2和ADMIRE5重建图像时其噪声分别减少18.35%和53.23%、18.82%和52.32%以及20.33%和53.79%。用ADMIRE0、ADMIRE2和ADMIRE5分别对CTP528模块图像进行重建,对其图像进行主观观察,可观察到的最小细节相同,均为7.0 LP/cm。结论:使用ADMIRE对图像进行重建可以在不影响图像清晰度的同时有效降低噪声,提高CT图像的低对比度分辨率,以帮助临床医师更好的观察组织密度相近的病灶,为临床医师诊断病情提供帮助。
