衰减
数学
先验与后验
继续
断层摄影术
理论(学习稳定性)
希尔伯特空间
反问题
断层重建
迭代重建
功能(生物学)
集合(抽象数据类型)
应用数学
数学分析
算法
计算机科学
人工智能
放射科
生物
认识论
进化生物学
机器学习
光学
物理
医学
哲学
程序设计语言
作者
Matías Courdurier,Frédéric Noo,Michel Defrise,Hiroyuki Kudo
出处
期刊:Inverse Problems
[IOP Publishing]
日期:2008-09-12
卷期号:24 (6): 065001-065001
被引量:135
标识
DOI:10.1088/0266-5611/24/6/065001
摘要
The case of incomplete tomographic data for a compactly supported attenuation function is studied. When the attenuation function is a priori known in a subregion, we show that a reduced set of measurements is enough to uniquely determine the attenuation function over all the space. Furthermore, we found stability estimates showing that reconstruction can be stable near the region where the attenuation is known. These estimates also suggest that reconstruction stability collapses quickly when approaching the set of points that are viewed under less than 180 degrees. This paper may be seen as a continuation of the work "Truncated Hilbert transform and Image reconstruction from limited tomographic data" that was published in Inverse Problems in 2006. This continuation tackles new cases of incomplete data that could be of interest in applications of computed tomography.
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