基于约束的MLEM图像重建算法

MLEM Image Reconstruction Algorithm Based on Physical Constraints

  • 摘要: 在编码孔γ相机的二维辐射图像重建中,最大似然期望最大化法(maximum likelihood expectation maximization,MLEM)相较传统线性算法能更好地抑制噪声,恢复出高质量的图像,但其缺点是收敛速度慢,且在投影数据有较高统计涨落或含有较大噪声时,迭代次数过高会导致重建图像中噪声急剧增加,图像质量转而变差。本文基于直接解调法思想对MLEM算法进行优化,在MLEM迭代重建过程中加入先验物理约束条件,并将交叉相关法重建结果作为迭代初值。仿真结果表明,添加约束后的MLEM迭代收敛速度得到加快,重建图像的收敛性显著改善。MLEM算法中添加合理约束条件是提高其重建图像质量的一种有效方法。

     

    Abstract: Maximum likelihood expectation maximization (MLEM) algorithm is an iterative method used to reconstruct radiation image in coded aperture gamma camera. This method works better in restraining noise when compared with traditional linear algorithms, but its convergence rate is low. Furthermore, noise in the reconstructed image will increase sharply for the excessive iterations when projection image contains too much noise. For the above mentioned reasons, it is necessary to optimize the MLEM algorithm and improve its performances. In this paper, physical constraints were added to the MLEM iteration based on direct demodulation method, and image data restored by the cross correlation method were used as the initial value of the new iteration. Simulation result shows that convergence rate of the iteration is accelerated and the convergence of the reconstructed image is better using the physical constraints during the iteration process. The method is effective to improve the quality of reconstructed image.

     

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