The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) ...The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.展开更多
现存大部分链路预测方法仅考虑单类型网络结构信息,无法同时保持一阶、局部和全局结构信息,导致预测精度下降.针对以上不足,提出对偶正则化非负矩阵分解的链路预测模型,同时保持一阶、局部和全局结构信息.首先,将无向无权的邻接矩阵映...现存大部分链路预测方法仅考虑单类型网络结构信息,无法同时保持一阶、局部和全局结构信息,导致预测精度下降.针对以上不足,提出对偶正则化非负矩阵分解的链路预测模型,同时保持一阶、局部和全局结构信息.首先,将无向无权的邻接矩阵映射到低维潜在空间保持网络一阶结构;其次,利用随机游走方法捕获整个网络节点相似度,再启用图正则化技术保持全局结构;此外,利用杰卡尔德系数获得局部相似度去探索网络局部结构;最后,将一阶、局部和全局结构信息相融合构建统一链路预测模型,并启用迭代更新规则学习模型参数获得局部最优.在6个真实网络上进行实验,运用AUC(areas under curve)和AUPR(areas under precision-recall)度量对所提模型进行评估,实验结果表明AUC和AUPR值分别提升了3.1%和8.9%.展开更多
基金National Natural Science Foundations of China(Nos.61362001,61102043,61262084)Technology Foundations of Department of Education of Jiangxi Province,China(Nos.GJJ12006,GJJ14196)Natural Science Foundations of Jiangxi Province,China(Nos.20132BAB211030,20122BAB211015)
文摘The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.
文摘现存大部分链路预测方法仅考虑单类型网络结构信息,无法同时保持一阶、局部和全局结构信息,导致预测精度下降.针对以上不足,提出对偶正则化非负矩阵分解的链路预测模型,同时保持一阶、局部和全局结构信息.首先,将无向无权的邻接矩阵映射到低维潜在空间保持网络一阶结构;其次,利用随机游走方法捕获整个网络节点相似度,再启用图正则化技术保持全局结构;此外,利用杰卡尔德系数获得局部相似度去探索网络局部结构;最后,将一阶、局部和全局结构信息相融合构建统一链路预测模型,并启用迭代更新规则学习模型参数获得局部最优.在6个真实网络上进行实验,运用AUC(areas under curve)和AUPR(areas under precision-recall)度量对所提模型进行评估,实验结果表明AUC和AUPR值分别提升了3.1%和8.9%.
