【摘要】 最大化后验(MAP)方法已经被广泛应用于解决图像重建的病态问题。先验项的选择一直是研究的热点,但是传统先验形式往往会导致重建图像模糊或者产生阶梯状伪影。本文针对传统先验形式存在的不足,提出了一种基于非广延熵先验的正电子发射成像(PET)迭代重建方法。该方法主要利用最小化非广延熵先验来消除先验信息和估计图像之间的不确定性。我们将此算法在体模图像上进行了测试,并与基于传统先验的MAP方法比较。实验表明,本文算法能更好抑制噪声,获得较好的重建图像质量。更多还原

【Abstract】 Maximum a Posteriori(MAP) method has been widely applied to the ill-posed problem of image reconstruction.The choice of prior is the crucial point on MAP methods.However,the most conventional priors will lead to a blurring of the whole image or cause ladder-like artifacts.We therefore proposed a Tsallis entropy-based prior for positron emission tomography(PET) iterative reconstruction in MAP framework.The method uses a Tsallis entropy-based prior to eliminate the uncertainty between prior information and the estimated images.We tested this method in the phantom image,compared it with the traditional prior methods.the results showed that the proposed algorithm could suppress noise and obtain better reconstructed image quality.更多还原