有序子集最小二乘OS—LS图像重建迭代算法

为推导一种新的快速图像迭代重建方法，首先将有序子集(ordered subsets，OS)技术应用到最小二乘图像重建迭代算法(least square reconstruction，LS)；然后对仿真Phantom模型数据和实际医用正电子发射断层成像仪(PET)数据进行重建，并研究了在不同子集划分下的重建结果，同时分析比较了不同子集的选取对OS—LS重建罔像质量以及重建收敛速度的影响。重建结果表明，这种基于有序子集的最小二乘图像重建迭代算法(OS—LS)具有较高的重建图像质量和较短的计算时间，相对于传统LS算法的重建，OS—LS的收敛速度加速了约L倍(L为子集个数)．其重建图像质量也好于传统的滤波反投影(FBP)方法的重建．町应用在PET图像重建中。

Study on Ordered Subsets-least Square Reconstruction of Image

In order to construct a new practical and fast iterative image reconstruction method, the ordered subsets(OS) technique is combined with the least square(LS) reconstruction of images in medical tomography. Reconstruction of simulated data and real positron emission tomography(PET) data shows that so accelerated OS-LS iterative image reconstruction method has a rapid convergent speed and higher spatial resolution. The reconstructed image quality and convergence speed by different subsets order are studied. As compared to the traditional LS reconstruction, OS-LS is L times faster, where L is the number of subsets, and the reconstructed images by OS-LS are better than the conventional FBP (filtered back-projection) as well. The conclusion is that the so proposed OS-LS reconstruction method can be used in real PET image reconstruction.