摘要
因高剂量CT对人体的损害较大,低剂量CT近年来已成为临床医学的一个热点,然而CT剂量的降低引起重建图像质量下降.为了解决低剂量CT重建图像受噪声污染严重的问题,本文根据低剂量CT投影的噪声特性,在原惩罚加权最小二乘降噪算法(PWLS)基础上,提出一种改进的基于模糊数学的惩罚加权最小二乘降噪算法.该算法采用统计建模的方法,将模糊数学的理论引入投影域惩罚加权最小二乘降噪算法中,利用隶属度函数改变权值.实验结果表明:改进的投影域惩罚加权最小二乘法降低了重建图像的噪声,同时有效地保护了重建图像的细节与边缘.
Low-dose CT recently has become popular for clinical medicine because high-dose CT does greater harm to human beings. However, the reduction of CT dose can degrade the image quality. In order to solve the noise problem about reconstructed images of low-dose CT, we presented a new improved penalized weight- ed least-squares （PWLS） algorithm, considering the noise properties of low-dose CT sinogram at the same time. The algorithm applied the fuzzy technology to the penalized weighted least - squares algorithm for the projection space and changed weight using the membership function. Simulations demonstrated that the modified PWLS not only removed noise efficiently but also protected image details and edges.
出处
《测试技术学报》
2011年第6期477-482,共6页
Journal of Test and Measurement Technology
基金
国家自然科学基金资助项目(61071192)
关键词
低剂量CT
投影域
惩罚加权最小二乘法
去噪
隶属度函数
low-dose computed tomography （CT）
sinograms
penalized weighted least-squares approach
noise reduction
membership function
