Convergence of the maximum likelihood reconstruction algorithm for emission computed tomography.
Convergence of the maximum likelihood reconstruction algorithm for emission computed tomography.
复制标题
发射计算机断层扫描最大似然重建算法的收敛。
DOI：
10.1088/0031-9155/32/4/005
复制
发表时间：
1987
影响因子：
3.5
通讯作者：
Coleman,RE
中科院分区：
文献类型：
--
作者：
FloydJr,CE;Jaszczak,RJ;Coleman,RE
发射计算机断层扫描（ECT）图像重建的最大似然估计（MLE）的收敛性能作为泊松噪声，精度的假设系统分辨率模型和迭代次数高达10000次迭代的函数进行评估。在ECT重建问题中，光子发射源的分布是估计从发射的光子通量的投影的测量。MLE算法寻求一个源分布，它将最大化与估计和测量投影相关的最大似然函数。单光子发射计算机断层扫描（SPECT）系统的系统传递函数的蒙特卡罗模型允许从已知的源分布模拟真实的投影数据。泊松噪声被添加到蒙特卡罗模拟。通过使用通过已知系统传递函数生成的已知源分布的投影数据，作者能够同时评估投影估计和源分布估计的收敛性。正如理论所预测的那样，对于测试的泊松噪声（高达10% RMS）和系统分辨率（真值的+或-10%）的所有组合，投影的估计值确实继续改善（或保持不变）。收敛源分布估计的真实值被发现只有低噪声（0.1%RMS）与正确的分辨率函数的10000次迭代。对于所有其他组合，存在一些最佳迭代（在30和400之间），在该最佳迭代之后，即使投影的估计得到改善，源估计也被降级。
Convergence properties of the maximum likelihood estimator (MLE) for emission computed tomographic (ECT) image reconstruction are evaluated as a function of Poisson noise, precision of the assumed system resolution model and iteration number up to 10000 iterations. In the ECT reconstruction problem, the photon-emitting source distribution is to be estimated from measurements of projections of the emitted photon flux. The MLE algorithm seeks a source distribution which will maximise the maximum likelihood function relating the estimated and the measured projections. A Monte Carlo model of the system transfer function of a single photon emission computed tomographic (SPECT) system allowed realistic projection data to be simulated from a known source distribution. Poisson noise was added to the Monte Carlo simulations. By using projection data from a known source distribution generated through a known system transfer function, the authors were able to simultaneously evaluate the convergence of both the projection estimations as well as the source distribution estimations. As predicted by theory, the estimates of the projections did continue to improve (or remain the same) for all combinations of Poisson noise (up to 10% RMS) and system resolution (+ or-10% of true value) tested. Convergence of source distribution estimates to the true value was found for up to 10000 iterations only for low noise (0.1% RMS) with the correct resolution function. For all other combinations, there was some optimum iteration (between 30 and 400) after which the source estimate was degraded even though the estimate of the projections was improved.