Mejia Jose, Ochoa Alberto, Mederos Boris
Department of Electrical and Computation Engineering, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico.
Department of Industrial and Systems, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico.
Entropy (Basel). 2019 Jan 18;21(1):83. doi: 10.3390/e21010083.
The reconstruction of positron emission tomography data is a difficult task, particularly at low count rates because Poisson noise has a significant influence on the statistical uncertainty of positron emission tomography (PET) measurements. Prior information is frequently used to improve image quality. In this paper, we propose the use of a field of experts to model a priori structure and capture anatomical spatial dependencies of the PET images to address the problems of noise and low count data, which make the reconstruction of the image difficult. We reconstruct PET images by using a modified MXE algorithm, which minimizes a objective function with the cross-entropy as a fidelity term, while the field of expert model is incorporated as a regularizing term. Comparisons with the expectation maximization algorithm and a iterative method with a prior penalizing relative differences showed that the proposed method can lead to accurate estimation of the image, especially with acquisitions at low count rate.
正电子发射断层扫描（PET）数据的重建是一项艰巨的任务，特别是在低计数率情况下，因为泊松噪声对PET测量的统计不确定性有显著影响。先验信息经常被用来提高图像质量。在本文中，我们提出使用专家场来对先验结构进行建模，并捕捉PET图像的解剖学空间依赖性，以解决噪声和低计数数据问题，这些问题使得图像重建变得困难。我们使用一种改进的MXE算法重建PET图像，该算法以交叉熵作为保真度项来最小化一个目标函数，同时将专家场模型作为正则化项纳入其中。与期望最大化算法和一种带有先验惩罚相对差异的迭代方法的比较表明，所提出的方法能够准确估计图像，特别是在低计数率采集情况下。
