摘要
有序子集期望最大化(OSEM)迭代算法是近年来发展较快的一种迭代类算法。但该算法在迭代过程中容易产生条纹状伪影、金属伪影或者散射伪影。构造了平滑约束矩阵作为先验信息引入到重建迭代过程,建立了一种平滑约束OSEM(SC-OSEM)迭代重建算法。分别将中值滤波、全变差最小(TVM)方法作为平滑约束条件,通过数值模拟,针对不完备理想投影数据、含金属不完备投影数据、含噪声不完备投影数据三种情况,重建出了与原始模型一致性较好的计算机层析成像技术(CT)图像,比单独OSEM迭代算法重建质量高,并且发现中值滤波约束重建图像的整体噪声较小,TVM算法使金属边界更清晰,表明SC-OSEM迭代重建算法是一种精度高、适应性较强的CT重建算法。
Order subset expectation maximization （OSEM） iterative algorithm is rapidly developed in recent years. But this algorithm is easy to generate some striation artifact, metal artifact or scattering artifact during iterative process. So a new iterative method named smooth constrained OSEM （SC-OSEM） is built, which utilizes smoothing constrained matrix as priori information in OSEM reconstruction. Median filtering algorithm and total variation minimization algorithm （TVM） are introduced as smoothing constrained conditions. The images reconstructed by computer tomography are consistent well with the initial model for the situations of incomplete projection data with ideal, metal, and noise, whose reconstruction qualities are better than only OSEM iterative algorithm. Median filtering constraint makes reconstructed image denoised, while TVM makes metal boundary clearer. These results manifest that SC-OSEM iterative algorithm is an adaptable and high precision CT reconstruction method.
出处
《激光与光电子学进展》
CSCD
北大核心
2017年第2期158-165,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(11275155)
关键词
图像处理
有序子集期望最大化算法
平滑约束
中值滤波算法
全变差最小算法
image processing
order subset expectation maximization algorithm
smoothing constrain
median filtering algorithm
total variation minimization algorithm
