List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET
List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET
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DOI：
10.1109/42.700734
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发表时间：
1998-04-01
影响因子：
10.6
通讯作者：
Barrett, HH
中科院分区：
文献类型：
--
作者：
Parra, L;Barrett, HH
利用Barrett等人最近提出的列表模式最大似然(ML)信源重构理论。在文献[1]的基础上，本文提出了相应的期望最大化(EM)算法，以及一种在ML估计时估计噪声性质的方法。在测量空间的维度阻碍测量数据入库的情况下，列表模式ML是有意义的。在可以通过包括由给定检测器提供的更多测量坐标来获得更好的正演模型的情况下，这可能是有利的。可以从费舍尔信息矩阵(FIM)计算探测器性能的不同品质因数。本文使用观测到的FIM，它需要一个单一的数据集，因此，避免了昂贵的集合统计，所提出的技术被证明是一个理想的二维正电子发射断层扫描(PET)[2-D PET]探测器。我们从模拟数据中计算了通过包含符合量子的飞行时间而获得的改善的图像质量。
Using a theory of list-mode maximum-likelihood (ML) source reconstruction presented recently by Barrett et al. [1], this paper formulates a corresponding expectation-maximization (EM) algorithm, as well as a method for estimating noise properties at the ML estimate, List-mode ML is of interest in cases where the dimensionality of the measurement space impedes a binning of the measurement data. It can be advantageous in cases where a better forward model can be obtained by including more measurement coordinates provided by a given detector. Different figures of merit for the detector performance can be computed from the Fisher information matrix (FIM). This paper uses the observed FIM, which requires a single data set, thus, avoiding costly ensemble statistics, The proposed techniques are demonstrated for an idealized two-dimensional (2-D) positron emission tomography (PET) [2-D PET] detector. We compute from simulation data the improved image quality obtained by including the time of flight of the coincident quanta.