Gamma regularization based reconstruction for low dose CT
Gamma regularization based reconstruction for low dose CT
复制标题
基于伽马正则化的低剂量 CT 重建
DOI：
10.1088/0031-9155/60/17/6901
复制
发表时间：
2015-09-07
影响因子：
3.5
通讯作者：
Coatrieux, Jean-Louis
中科院分区：
文献类型：
--
作者：
Zhang, Junfeng;Chen, Yang;Coatrieux, Jean-Louis
减少计算机断层扫描中的辐射是当今放射学的主要关注点。低剂量计算机断层扫描（LDCT）为解决这一问题提供了一种有效的方法。然而，在低剂量扫描协议下（例如降低的管电流或电压值），重建的 CT 图像中会观察到更严重的噪声。在本文中，我们提出了一种基于伽马正则化的 LDCT 图像重建算法。该解决方案非常灵活，并且在基于 l(0)-范数和 l(1)-范数的正则化之间提供了良好的平衡。我们使用来自模拟体模和扫描 Catphan 体模的投影数据来评估所提出的方法。定性和定量结果表明，与其他规范相比，基于伽玛正则化的重建在边缘保留和噪声抑制方面都能表现更好。
Reducing the radiation in computerized tomography is today a major concern in radiology. Low dose computerized tomography (LDCT) offers a sound way to deal with this problem. However, more severe noise in the reconstructed CT images is observed under low dose scan protocols (e.g. lowered tube current or voltage values). In this paper we propose a Gamma regularization based algorithm for LDCT image reconstruction. This solution is flexible and provides a good balance between the regularizations based on l(0)-norm and l(1)-norm. We evaluate the proposed approach using the projection data from simulated phantoms and scanned Catphan phantoms. Qualitative and quantitative results show that the Gamma regularization based reconstruction can perform better in both edge-preserving and noise suppression when compared with other norms.