Time-of-flight PET image reconstruction using origin ensembles.
Time-of-flight PET image reconstruction using origin ensembles.
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DOI：
10.1088/0031-9155/60/5/1919
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发表时间：
2015-03-07
影响因子：
3.5
通讯作者：
Prevrhal S
中科院分区：
文献类型：
--
作者：
Wülker C;Sitek A;Prevrhal S
原点集成(OE)算法是一种新的用于发射层析成像数据最小均方误差(MMSE)重建的统计方法。该方法允许人们完全在图像域中执行重建，即不使用正向和反投影操作。我们在列表模式(LM)飞行时间(TOF)PET重建的背景下研究了OE算法。在这篇文章中，我们提供了MMSE重建的一般介绍，并对OE算法进行了统计严格的推导。我们展示了如何将TOF信息有效地合并到重建过程中，以及如何校正随机重合和分散事件。为了验证使用OE算法重建LM-TOF MMSE的可行性，我们将MMSE-OE和标准最大似然期望(ML-EM)重建应用于具有临床PET检查中典型计数的LM-TOF模型数据。分析了OE算法的收敛行为，并与EM算法的重建时间和图像质量进行了比较。总之，在重建过程中，MMSE-OE对比度恢复(CRV)基本保持不变，而背景变异性(BV)随着OE迭代次数的增加而逐渐降低。与相应的ML-EM图像相比，最终的MMSE-OE图像显示出更低的BV和更低的CRV。OE算法的重建时间大约要长1.3倍。同时，OE算法可以内在地提供所获取数据的全面统计表征。这种特征可以用于进一步的数据处理，例如在动力学分析和图像配准中，使OE算法在各种应用中成为一种有前途的方法。
The origin ensemble (OE) algorithm is a novel statistical method for minimummean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum- likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.
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影响因子：
10.6
作者：
Sitek A
通讯作者：
Sitek A
影响因子：
--
作者：
SHANNON, CE
通讯作者：
SHANNON, CE
影响因子：
10.6
作者：
Shepp, L A;Vardi, Y
通讯作者：
Vardi, Y
影响因子：
10.6
作者：
Sitek A;Moore SC
通讯作者：
Moore SC
影响因子：
3.5
作者：
Sitek, A.
通讯作者：
Sitek, A.