正电子发射断层图像重建中基于有序子集惩罚最小二乘算法的应用

Application of penalized least-squares algorithm in positron emission tomography image reconstruction based ordered subsets

为了解决正电子断层成像中传统迭代重建算法不能有效抑制噪声和收敛速度慢的问题,在最小二乘算法中引入二次平滑先验惩罚项,并结合有序子集加速方法用于正电子发射断层图像重建,形成了有序子集惩罚最小二乘算法。实验结果表明,有序子集惩罚最小二乘算法相对于最大似然估计等算法,不仅能够有效地抑制噪声,重建出质量更好的图像,而且较大地提高了收敛速度。

The traditional iterative reconstruction algorithms of positron emission tomography can not effectively suppress the noise and have slow convergence. In order to solve the problem, we introduce a quadratic membrane smoothing penalty prior term into the least-squares algorithm, and combine with ordered subsets （OS） method, forming the penalized least-squares algorithm in positron emission tomography image reconstruction based ordered subsets （OS-PLS）. Results of computer simulated demonstrated that compared with the other reconstruction algorithms such as MLEM, OS-PLS not only availably suppress the noise and reconstruct a higher quality image, but also enhance the convergence preferable.