南方医科大学学报

南方医科大学学报 ›› 2015, Vol. 35 ›› Issue (10): 1446-.

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一种结合低秩与稀疏惩罚的PET动态图像重建方法

魏夏平，江学文，马晓勉，路利军   

  • 出版日期:2015-10-20 发布日期:2015-10-20

Reconstruction of dynamic positron emission tomographic images by exploiting low
rank and sparse penalty

  • Online:2015-10-20 Published:2015-10-20

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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。

Abstract: Objective To propose a new method for dynamic positron emission tomographic (PET) image reconstruction using
low rank and sparse penalty (L&S). Methods The L&S 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 82Rb
myocardial perfusion image. The L&S reconstruction method was compared with maximum likelihood expectation
maximization (MLEM) method, low-rank penalty method and sparse penalty method. Results The L&S reconstruction
method had the smallest MSE and well maintained the feature information. The polar map created by L&S method was the
most similar with the reference actual polar map. Conclusion L&S reconstruction method is better than the other three
methods in both visual and quantitative analysis of the PET images.