基于新的非局部先验模型的Bayesian低剂量CT重建算法

Bayesian Reconstruction Algorithm for Low-dose CT Based on New Nonlocal Prior Model

为了改善低剂量CT重建图像质量,在传统非局部先验的基础上,提出了一种基于投影对称性的改进非局部先验模型。基于该先验模型构造了一种贝叶斯(Bayesian)重建算法,并将其应用到低剂量CT投影数据降噪中,通过滤波反投影算法重建出图像。仿真实验结果表明,本文所提出的算法较基于传统先验模型的重建算法,能在去除噪声与保持边缘之间取得较好的平衡。

In order to improve the quality of low-dose CT reconstructed image, this study proposes a projection symmetry-based modified nonlocal prior model based on the traditional nonlocal prior model. Then, a Bayesian reconstruction algorithm is built combined with this prior model, and it is applied to the noise removal of the low-dose CT projection data. The reconstructed images are obtained by the filtered back-projection （FBP） algorithm. The results of simulated experiment show the proposed algorithm, compared with the algorithms based on the traditional priors, can achieve a superior balance between suppressing noise and preserving edges.