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基于新的非局部先验模型的Bayesian低剂量CT重建算法
引用本文:姜盛杰. 基于新的非局部先验模型的Bayesian低剂量CT重建算法[J]. CT理论与应用研究, 2014, 23(3): 395-402.
作者姓名:姜盛杰
作者单位:1.丹东奥龙射线仪器集团有限公司, 辽宁 丹东 118009
摘    要: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。

关 键 词:重建算法  低剂量CT  非局部先验模型  贝叶斯
收稿时间:2014-01-02

Bayesian Reconstruction Algorithm for Low-dose CT Based on New Nonlocal Prior Model
JIANG Sheng-jie. Bayesian Reconstruction Algorithm for Low-dose CT Based on New Nonlocal Prior Model[J]. CT Theory and Applications, 2014, 23(3): 395-402.
Authors:JIANG Sheng-jie
Affiliation:1.Dandong Aolong Radiative lnstrument Co.ltd., Dandong 118009, China
Abstract: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.
Keywords:reconstruction algorithm  low-dose computed tomography  nonlocal prior model  Bayesian
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