基于相关系数和双向扩散结合的优质正电子发射断层重建算法

High quality positron emission tomography reconstruction algorithm based on correlation coefficient and forward-and-backward diffusion

在正电子发射断层成像(PET)中,传统迭代算法会造成重建图像细节信息丢失或目标边界模糊。为了解决上述问题,提出一种基于相关系数和双向扩散结合的优质中值先验(MP)重建算法。首先,引入特征因子相关系数来表征图像局部灰度统计信息,构造出结合相关系数的双向扩散模型;其次,考虑到双向模型对背景和边缘区别处理的优点,将新模型应用到中值先验分布的最大后验重建算法中,形成基于双向扩散的中值先验重建算法。实验结果表明,该算法在去除噪声的同时能够较好地保持图像中的目标边界信息,信噪比(SNR)和均方误差(RMSE)的变化也能直观体现重建图像质量的提高。

In Positron Emission Tomography （PET） computed imaging, traditional iterative algorithms have the problem of details loss and fuzzy object edges. A high quality Median Prior （MP） reconstruction algorithm based on correlation coefficient and Forward-And-Backward （FAB） diffusion was proposed to solve the problem in this paper. Firstly, a characteristic factor called correlation coefficient was introduced to represent the image local gray information. Then through combining the correlation coefficient and forward-and-backward diffusion model, a new model was made up. Secondly, considering that the forward-and-backward diffusion model has the advantages of dealing with background and edge separately, the proposed model was applied to Maximum A Posterior （MAP） reconstruction algorithm of the median prior distribution, thus a median prior reconstruction algorithm based on forward-and-backward diffusion was obtained. The simulation results show that, the new algorithm can remove the image noise while preserving object edges well. The Signal-to-Noise Ratio （SNR） and Root Mean Squared Error （RMSE） also show visually the improvement of the reconstructed image quality.