A MAXIMUM-LIKELIHOOD METHOD FOR REGION-OF-INTEREST EVALUATION IN EMISSION TOMOGRAPHY
A MAXIMUM-LIKELIHOOD METHOD FOR REGION-OF-INTEREST EVALUATION IN EMISSION TOMOGRAPHY
复制标题
DOI：
10.1097/00004728-198607000-00021
复制
发表时间：
1986-07-01
影响因子：
1.3
通讯作者：
CARSON, RE
中科院分区：
文献类型：
--
作者：
CARSON, RE
提出了一种最大似然估计方法，称为ML-ROI算法，用于计算发射断层扫描的感兴趣区域（ROI）值。EM算法用于直接从层析投影数据中估计ROI值，给定所有ROI的位置，大小和形状。该算法需要详细说明影响投影测量的物理因素的模型，包括分辨率和衰减。ML-ROI算法还提供了ROI估计器（协方差矩阵）可变性的估计。用仿真和仿真数据对该算法进行了测试，并与滤波后的反向投影（FBP）图像ROI估计策略进行了比较。ML-ROI估计是无偏的，即部分体积效应被消除了。除了小于检测器分辨率的区域外，ML估计的可变性与有偏FBP估计相当或小于。ML-ROI算法的计算时间在5 ~ 10 s/迭代之间。评估了该算法对roi的位置和大小的错误定义的敏感性。
A maximum likelihood (ML) estimation method, called the ML-ROI algorithm, is presented for the calculation of region-of-interest (ROI) values from emission tomography scans. The EM algorithm is used to directly estimate ROI values from tomographic projection data given the location, size, and shape of all ROIs. The algorithm requires the specification of a detailed model of the physical factors contributing to projection measurements including resolution and attenuation. The ML-ROI algorithm also provides an estimate of the variability of the ROI estimator (covariance matrix). The algorithm was tested with simulation and phantom data and compared with ROI estimation strategies using filtered backprojection (FBP) images. The ML-ROI estimates were unbiased, i.e., the partial volume effect was eliminated. Except for regions smaller than the detector resolution, the variability of the ML estimates was comparable to or less than the biased FBP estimators. Computation time for the ML-ROI algorithm was between 5 and 10 s/iteration. An evaluation of the sensitivity of the algorithm to misdefinition of the location and size of the ROIs was also performed.