基于小波和各向异性扩散的 PET 图像 MLEM 重建算法

摘要

In positron emission tomography ( PET) imaging, traditional maximum likelihood expectation maximisation ( MLME) algorithm has the deficiencies of converging slowly and can not suppress noise effectively .To address this problem , usually the regularisation term would be introduced to iterative process to improve the reconstruction performance of MLEM .In this paper , we propose a new denoising algorithm which is based on wavelet shrinkage and anisotropic diffusion , and combine this algorithm with the MLEM algorithm to form a novel PET reconstruction method.Experimental results show that this algorithm can obtain higher SNR and superior visual effect on images while reducing the complexity and keeping higher convergence rate .%在正电子发射断层成像中，经典的MLEM（Maximum Likelihood Expectation Maximization ）算法具有收敛速度慢、不能有效抑制噪声的不足。为了解决该问题，通常在迭代过程中加入正则项来改善MLEM的重建性能。提出一种新的基于小波收缩和各向异性扩散的去噪算法，将该算法与MLEM算法结合起来形成一种新的PET（ Positron Emission Tomography ）重建方法。实验结果表明，该算法在降低复杂性、保持较高收敛速度的同时，能获得较高的信噪比和较好的图像视觉效果。