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

摘要

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.%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。