Level set method for positron emission tomography.
Level set method for positron emission tomography.
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DOI：
10.1155/2007/26950
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发表时间：
2007
影响因子：
7.6
通讯作者：
Tai XC
中科院分区：
文献类型：
--
作者：
Chan TF;Li H;Lysaker M;Tai XC
在正电子发射断层扫描（PET）中，一种放射性化合物被注射到体内以促进组织依赖的发射率。期望最大化（EM）重建算法是一种迭代技术，它估计提供最佳拟合解的浓度系数，例如，最大似然估计。在本文中，我们将EM算法与水平集方法相结合。采用水平集方法捕获粗尺度信息和浓度系数的不连续。水平集公式的一个内在优势是解剖信息可以以一种简单自然的方式有效地结合和使用。我们使用多级集公式来表示场景中物体的几何形状。该算法可以应用于任何PET配置，无需进行重大修改。
In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. Expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate. In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way. We utilize a multiple level set formulation to represent the geometry of the objects in the scene. The proposed algorithm can be applied to any PET configuration, without major modifications.