基于多分辨率的PET图像优质有序子集最大期望重建算法

High quality ordered subset expectation maximization reconstruction algorithm based on multi-resolution for PET images

在正电子发射断层扫描(PET)成像中,最大似然期望(MLEM)算法因不能有效地抑制噪声、收敛速度慢而难以直接应用于临床。有序子集最大期望(OSEM)算法具有较快的收敛速度,但是在迭代一定次数之后重建质量会迅速下降。针对此问题,将多分辨率技术引入到有序子集最大期望重建算法的子集中,以此抑制噪声,同时稳定求解过程。实验结果表明,新的重建算法克服了传统算法图像退化的缺点,并具有加快算法收敛速度的优点,能获得较高的信噪比(SNR)和较好的图像视觉效果。

In Positron Emission Tomography （PET） imaging, Maximum Likelihood Expectation Maximization （MLEM） algorithm cannot be directly applied to clinical diagnosis due to suppressing noise ineffectively and converging slowly. Although Ordered Subset Expectation Maximization （OSEM） algorithm converges fast, it will lead to a significant decline in the quality of the reconstructed image. To address this problem, multi-resolution technology was introduced into the subset of the OSEM reconstruction algorithm to suppress noise and stabilize solving process. The experimental results indicate that the new algorithm overcomes the drawback of the traditional algorithm on degrading the reconstructed image and has the advantage of fast convergence. The proposed reconstruction algorithm can obtain a higher Signal-to-Noise Ratio （SNR） and a superior visual effect.