图像重建的统计自适应子集算法

Statistical Self-Adaptive Ordered Subsets Algorithm for Image Reconstruction

有序子集算法大大提高了图像重建迭代算法的收敛速度.增加子集的数目可以加速收敛,但图像质量会由于子集内缺少统计信息而下降.提出了一种基于假设检验的子集划分方法,建立了检验统计量,给出了算法的迭代公式.该算法可以根据用户定义的显著性水平,在每次迭代时自动调节子集的个数,生成含有相同统计信息量的子集.实验结果表明:该方法可以在少数次迭代后得到较高质量的重建图像.

The convergence rate of iterative algorithm for image reconstruction can be accelerated by ordered subsets method, but the image quality degrades due to lack of statistical information within subsets. An approach of subset partition is proposed based on hypothesis test. Two test statistics are established and the iterative formula is given. The method can automatically adjust the number of the subset for each iterative according to the significant level demanded by user and form the subset with the same statistical information content. The experimental results demonstrate that this algorithm can converge faster and provide high-quality reconstructed images after a few iterations.