摘要
目的提出一种结合低秩与稀疏惩罚的PET动态图像重建方法(L&S)。方法建立L&S重建模型,利用split Bregman法来最优化求解代价函数。采用单房室模型仿真一套PET心肌82Rb灌注图像,将L&S重建方法与最大似然期望值法(MLEM)、低秩惩罚和稀疏惩罚重建方法比较。结果 L&S方法重建的图像的均方误差(MSE)最小,并且保留了更多图像特征。另外L&S重建得到的靶心图和参考组的靶心图最相近。结论 L&S重建方法无论是在直观视觉上,还是定量分析上都优于另外3种方法。
Objective To propose a new method for dynamic positron emission tomographic（PET） image reconstruction using low rank and sparse penalty（LS）. Methods The LS reconstruction model was established and the split Bregman method was used to solve the optimal cost function. The one-tissue compartment model was used to simulate a set of PET 82 Rb myocardial perfusion image. The LS reconstruction method was compared with maximum likelihood expectation maximization（MLEM） method, low-rank penalty method and sparse penalty method. Results The LS reconstruction method had the smallest MSE and well maintained the feature information. The polar map created by LS method was the most similar with the reference actual polar map. Conclusion LS reconstruction method is better than the other three methods in both visual and quantitative analysis of the PET images.
出处
《南方医科大学学报》
CAS
CSCD
北大核心
2015年第10期1446-1450,共5页
Journal of Southern Medical University
基金
国家自然科学基金(81501541)
广东省自然科学基金(2014A030310243)
教育部高等学校博士学科专项科研基金(20134433120017)
广东省医学科研基金(B2014240)~~
关键词
低秩
稀疏
重建
心肌灌注
low rank
sparse
reconstruction
myocardial perfusion
