A non-parametric bootstrap approach for analysing the statistical properties of SPECT and PET images
A non-parametric bootstrap approach for analysing the statistical properties of SPECT and PET images
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DOI：
10.1088/0031-9155/47/10/311
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发表时间：
2002-05-21
影响因子：
3.5
通讯作者：
Buvat, I
中科院分区：
文献类型：
--
作者：
Buvat, I
了解重建的单光子发射计算机断层扫描 (SPECT) 和正电子发射断层扫描 (PET) 图像的统计特性将有助于优化采集和图像处理协议。我们描述了一种非参数引导方法，可以准确估计 SPECT 或 PET 图像的统计特性，无论投影和重建算法中的噪声特性如何。使用分析模拟和真实 PET 数据，该方法可以准确预测统计特性。包括线性（滤波反投影）和非线性（有序子集期望最大化）重建算法的重建像素值的方差和协方差。
Knowledge of the statistical properties of reconstructed single photon emission computed tomography (SPECT) and positron emission tomography (PET) images would be helpful for optimizing acquisition and image processing protocols, We describe a non-parametric bootstrap approach to accurately estimate the statistical properties of SPECT or PET images whatever the noise properties in the projections and the reconstruction algorithm. Using analytical simulations and real PET data, this method is shown to accurately predict the statistical properties. including the variance and covariance, of reconstructed pixel values for both linear (filtered backprojection) and non-linear (ordered subset expectation maximization) reconstruction algorithms.