基于边缘检测算子的Huber正则化阈值选择方法在低剂量CT重建中的应用  

Edge-detecting operator-based selection of Huber regularization threshold for low-dose computed tomography imaging

作　　者：张善立 张华[1] 胡德斌[1] 曾栋[1] 边兆英[1] 路利军[1] 马建华[1] 黄静[1] 

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515

出　　处：《南方医科大学学报》2015年第3期375-379,共5页Journal of Southern Medical University

基　　金：国家自然科学基金(81101046;81371544);国家973重点基础研究发展计划(2010CB732503);国家科技支撑计划(2011BAI12B03)~~

摘　　要：目的研究两种不同的Huber正则化阈值自适应选取方法及其在低剂量CT迭代重建中的应用。方法针对低剂量CT重建采用基于Huber正则化的迭代重建技术,Huber正则化阈值的选取分别基于全局和局部边缘保持算子。结果仿真数据的实验结果表明,两类Huber正则化阈值自适应选取方法均能较好地抑制重建图像中的噪声和伪影。结论两类Huber正则化阈值自适应选择方法均可实现低剂量CT优质重建。Objective To compare two methods for threshold selection in Huber regularization for low- dose computed tomography imaging. Methods Huber regularization- based iterative reconstruction（IR） approach was adopted for low- dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge- detecting operators. Results The experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal. Conclusion Both of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.

关 键 词：低剂量CT 迭代重建 Huber正则化 阈值选择 

分 类 号：R814.2[医药卫生—影像医学与核医学]