将配准的解剖图像作为先验信息指导PET图像重建已有广泛的研究.基于非局部均值(nonlocal means)滤波和解剖图像的区域信息,提出一种解剖自适应的非局部先验(anatomically adaptive nonlocal prior,AANLP)模型.新模型中的信息来自一个较大的非局部邻域内灰度值的加权差,其权值通过计算两个像素的相似性获得.权值参数通过利用解剖图像的区域信息进行自适应迭代估计.在PET图像的重建过程中,AANLP模型自适应地用于每一个解剖区域.构建两步式重建策略,用于图像重建和参数估计.仿真数据重建结果表明,AANLP具有很好的保持边缘效果,并且能鲁棒地产生最高的病灶对比度.%The incorporation of registered anatomical image as a prior to guide PET image reconstruction has been reported in many previous studies. Based on the nonlocal means filter and the regional information of anatomical image, an anatomically adaptive nonlocal prior (AANLP) is proposed. The information in this prior model comes from weighted differences between pixel intensities with in a nonlocal neighborhood. The weights of each pixel depend on its similarity with respect to the other pixels. The regional information of anatomical image is used to estimate a smoothing parameter which controls the decay of similarity function. This prior is determined and applied adaptively to each anatomical region on the PET image for the iteration in the reconstruction process. A two-step reconstruction scheme using the AANLP is proposed to update the image and estimate the parameter. The simulation results show that the AANLP reconstruction can dramatically preserve the edges and yield overall higher lesion-to-background contrast.
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