Cheng Ju-Chieh, Rahmim Arman, Blinder Stephan, Camborde Marie-Laure, Raywood Kelvin, Sossi Vesna
Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada.
Phys Med Biol. 2007 Apr 21;52(8):2089-106. doi: 10.1088/0031-9155/52/8/004. Epub 2007 Mar 23.
We describe an ordinary Poisson list-mode expectation maximization (OP-LMEM) algorithm with a sinogram-based scatter correction method based on the single scatter simulation (SSS) technique and a random correction method based on the variance-reduced delayed-coincidence technique. We also describe a practical approximate scatter and random-estimation approach for dynamic PET studies based on a time-averaged scatter and random estimate followed by scaling according to the global numbers of true coincidences and randoms for each temporal frame. The quantitative accuracy achieved using OP-LMEM was compared to that obtained using the histogram-mode 3D ordinary Poisson ordered subset expectation maximization (3D-OP) algorithm with similar scatter and random correction methods, and they showed excellent agreement. The accuracy of the approximated scatter and random estimates was tested by comparing time activity curves (TACs) as well as the spatial scatter distribution from dynamic non-human primate studies obtained from the conventional (frame-based) approach and those obtained from the approximate approach. An excellent agreement was found, and the time required for the calculation of scatter and random estimates in the dynamic studies became much less dependent on the number of frames (we achieved a nearly four times faster performance on the scatter and random estimates by applying the proposed method). The precision of the scatter fraction was also demonstrated for the conventional and the approximate approach using phantom studies.
我们描述了一种普通泊松列表模式期望最大化（OP-LMEM）算法，该算法采用基于单散射模拟（SSS）技术的基于正弦图的散射校正方法和基于方差减少延迟符合技术的随机校正方法。我们还描述了一种用于动态PET研究的实用近似散射和随机估计方法，该方法基于时间平均散射和随机估计，然后根据每个时间帧的真符合和随机事件的全局数量进行缩放。将使用OP-LMEM实现的定量准确性与使用具有类似散射和随机校正方法的直方图模式3D普通泊松有序子集期望最大化（3D-OP）算法获得的定量准确性进行比较，结果显示二者具有出色的一致性。通过比较时间-活度曲线（TAC）以及从传统（基于帧）方法和近似方法获得的动态非人灵长类动物研究的空间散射分布，测试了近似散射和随机估计的准确性。结果发现二者具有出色的一致性，并且动态研究中散射和随机估计的计算所需时间对帧数的依赖性大大降低（通过应用所提出的方法，我们在散射和随机估计方面实现了近四倍的更快性能）。还使用体模研究证明了传统方法和近似方法在散射分数方面的精度。
