基于结构先验约束的PET图像重建研究

Geometrical Structure Prior Constrained Dynamic PET Image Reconstruction

提出了一种通过对空间相关的CT解剖模版中所体现的不同组织区域位置信息赋予不同的药物运转规律特性,在状态空间方法进行PET图像重建的基础上,建立加入结构约束的动态PET重建框架的方法。考虑到可能出现的结构信息与PET浓度分布不匹配的情况,对低放射性浓度区域进行一定处理,使得该PET重建浓度框架具有一定的区域自适应能力。所采用的滤波器对系统和噪声不做任何假设,只需假定噪声扰动能量有限,更好地适应PET重建系统。并且,利用状态方程能够在时间轴上对图像进行预估反馈的特性,加入时间域上的约束,将全帧图像作为整体的一个时间-空间信号,大大增强了时间关联性。最后,开展蒙特卡洛模拟实验对该算法在改善PET重建图像的分辨率和图像边界模糊性方面进行验证。

In order to improve the spatial resolution and regularization of ill--conditioning problem, we expand our investigation into structural constraint. With the advantages of robust nature of filter and easy implementation of state space approach, an ilproved algorithm for PET image reconstruction is proposed by using segmented anatomical template that provided by other high quality imaging technology such as CT. Compared with other algorithms, experiments conducted by Monte carlo simulations indicate a persuasive assessment that the proposed strategy suppresses noise well, while the edges, boundary information and other details remain clear.