加权
卷积(计算机科学)
迭代重建
扫描仪
图像质量
数学
算法
数据集
集合(抽象数据类型)
图像(数学)
数学分析
计算机科学
人工智能
计算机视觉
物理
统计
程序设计语言
人工神经网络
声学
摘要
The problem of using a divergent fan beam convolution reconstruction algorithm in conjunction with a minimal complete (180° plus the fan angle) data set is reviewed. It is shown that by proper weighting of the initial data set, image quality essentially equivalent to the quality of reconstructions from 360° data sets is obtained. The constraints on the weights are that the sum of the two weights corresponding to the same line‐integral must equal one, in regions of no data the weights must equal zero, and the weights themselves as well as the gradient of the weights must be continuous over the full 360°. After weighting the initial data with weights that satisfy these constraints, image reconstruction can be conveniently achieved by using the standard (hardwired if available) convolver and backprojector of the specific scanner.
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