In this article we introduce an exact backprojection filtered (BPF) type reconstruction algorithm for cone-beam scans based on Zou and Pan’s work. The algorithm can reconstruct images using only the projection data p...In this article we introduce an exact backprojection filtered (BPF) type reconstruction algorithm for cone-beam scans based on Zou and Pan’s work. The algorithm can reconstruct images using only the projection data passing through the parallel PI-line segments in reduced scans. Computer simulations and practical experiments are carried out to evaluate this algorithm. The BPF algorithm has a higher computational efficiency than the famous FDK algorithm. The BPF algorithm is evaluated using the practical CT projection data on a 450 keV X-ray CT system with a flat-panel detector (FPD). From the practical experiments, we get the spatial resolution of this CT system. The algo- rithm could achieve the spatial resolution of 2.4 lp/mm and satisfies the practical applications in industrial CT inspec- tion.展开更多
基金Supported by a grant from the Ph.D. Programs Foundation of Ministry of Education of China (No. 20030003074) and the National Natural Science Founda-tion of China (No. 10575059).
文摘In this article we introduce an exact backprojection filtered (BPF) type reconstruction algorithm for cone-beam scans based on Zou and Pan’s work. The algorithm can reconstruct images using only the projection data passing through the parallel PI-line segments in reduced scans. Computer simulations and practical experiments are carried out to evaluate this algorithm. The BPF algorithm has a higher computational efficiency than the famous FDK algorithm. The BPF algorithm is evaluated using the practical CT projection data on a 450 keV X-ray CT system with a flat-panel detector (FPD). From the practical experiments, we get the spatial resolution of this CT system. The algo- rithm could achieve the spatial resolution of 2.4 lp/mm and satisfies the practical applications in industrial CT inspec- tion.
文摘针对现有变电站碳排放量预测模型存在考虑指标较少、数据更新慢等问题,本文提出一种基于改进萤火虫算法(improved firefly algorithm,IFA)优化反向传播(back propagation,BP)神经网络的变电站碳排放预测模型。首先,针对萤火虫算法(firefly algorithm,FA)收敛速度过慢以及易陷入局部最优等问题,引入教与学因子,修改萤火虫位置更新过程,以提高群体适应度。其次,引入IFA算法对BP神经网络模型进行超参数寻优,并构建IFA-BP神经网络预测模型。然后,基于CRITIC法筛选预测模型输入层的关键碳排放指标。最后,利用训练集数据训练预测模型,基于训练好的模型对变电站的碳排放量进行预测。仿真结果表明,相较于3种对比方案,本文IFA-BP神经网络预测模型分别在均方根误差(root mean square error,RMSE)上降低59.61%、15.77%和26.65%,在决定系数(coefficient of determination,R^(2))上提高5.66%、1.46%和1.15%,充分验证了本文所提变电站碳排放预测模型的可行性与优越性。
