Zhang Hao, Zeng Dong, Zhang Hua, Wang Jing, Liang Zhengrong, Ma Jianhua
Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA.
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
Med Phys. 2017 Mar;44(3):1168-1185. doi: 10.1002/mp.12097.
Low-dose X-ray computed tomography (LDCT) imaging is highly recommended for use in the clinic because of growing concerns over excessive radiation exposure. However, the CT images reconstructed by the conventional filtered back-projection (FBP) method from low-dose acquisitions may be severely degraded with noise and streak artifacts due to excessive X-ray quantum noise, or with view-aliasing artifacts due to insufficient angular sampling. In 2005, the nonlocal means (NLM) algorithm was introduced as a non-iterative edge-preserving filter to denoise natural images corrupted by additive Gaussian noise, and showed superior performance. It has since been adapted and applied to many other image types and various inverse problems. This paper specifically reviews the applications of the NLM algorithm in LDCT image processing and reconstruction, and explicitly demonstrates its improving effects on the reconstructed CT image quality from low-dose acquisitions. The effectiveness of these applications on LDCT and their relative performance are described in detail.
由于对过量辐射暴露的担忧日益增加，低剂量X射线计算机断层扫描（LDCT）成像在临床上被强烈推荐使用。然而，通过传统的滤波反投影（FBP）方法从低剂量采集重建的CT图像，可能会由于过量的X射线量子噪声而严重退化，出现噪声和条纹伪影，或者由于角度采样不足而出现视图混叠伪影。2005年，非局部均值（NLM）算法作为一种非迭代的边缘保持滤波器被引入，用于对被加性高斯噪声破坏的自然图像进行去噪，并表现出卓越的性能。自那时起，它已被改编并应用于许多其他图像类型和各种逆问题。本文专门回顾了NLM算法在LDCT图像处理和重建中的应用，并明确展示了其对低剂量采集重建的CT图像质量的改善效果。详细描述了这些应用在LDCT上的有效性及其相对性能。
