Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
IEEE Trans Med Imaging. 2012 Oct;31(10):1977-88. doi: 10.1109/TMI.2012.2212203. Epub 2012 Aug 8.
Direct reconstruction of kinetic parameters from raw projection data is a challenging task in molecular imaging using dynamic positron emission tomography (PET). This paper presents a new optimization transfer algorithm for penalized likelihood direct reconstruction of nonlinear parametric images that is easy to use and has a fast convergence rate. Each iteration of the proposed algorithm can be implemented in three simple steps: a frame-by-frame maximum likelihood expectation-maximization (EM)-like image update, a frame-by-frame image smoothing, and a pixel-by-pixel time activity curve fitting. Computer simulation shows that the direct algorithm can achieve a better bias-variance performance than the indirect reconstruction algorithm. The convergence rate of the new algorithm is substantially faster than our previous algorithm that is based on a separable paraboloidal surrogate function. The proposed algorithm has been applied to real 4-D PET data.
从原始投影数据中直接重建动力学参数是使用动态正电子发射断层扫描（PET）进行分子成像的一项具有挑战性的任务。本文提出了一种新的优化传递算法，用于惩罚似然性非线性参数图像的直接重建，该算法易于使用且具有快速收敛速度。所提出算法的每次迭代都可以通过三个简单的步骤来实现：逐帧最大似然期望最大化（EM）样图像更新、逐帧图像平滑以及逐像素时间活动曲线拟合。计算机模拟表明，直接算法可以实现比间接重建算法更好的偏差-方差性能。新算法的收敛速度明显快于我们之前基于可分离抛物面替代函数的算法。该算法已应用于真实的 4D PET 数据。
