正电子发射断层扫描的交叉参考加权最小二乘估计。

Cross-reference weighted least square estimates for positron emission tomography.

作者信息

Lu H H, Chen C M, Yang I H

机构信息

Institute of Statistics, College of Science, National Chiao Tung University, Hsinchu, Taiwan.

出版信息

IEEE Trans Med Imaging. 1998 Feb;17(1):1-8. doi: 10.1109/42.668690.

DOI:10.1109/42.668690

PMID:9617903

Abstract

An efficient new method, termed as the cross-reference weighted least square estimate (WLSE) [CRWLSE], is proposed to integrate the incomplete local smoothness information to improve the reconstruction of positron emission tomography (PET) images in the presence of accidental coincidence events and attenuation. The algebraic reconstruction technique (ART) is applied to this new estimate and the convergence is proved. This numerical technique is based on row operations. The computational complexity is only linear in the sizes of pixels and detector tubes. Hence, it is efficient in storage and computation for a large and sparse system. Moreover, the easy incorporation of range limits and spatially variant penalty will not deprive the efficiency. All this makes the new method practically applicable. An automatically data-driven selection method for this new estimate based on the generalized cross validation is also studied. The Monte Carlo studies demonstrate the advantages of this new method.

摘要

提出了一种高效的新方法，称为交叉参考加权最小二乘估计（WLSE）[CRWLSE]，用于整合不完整的局部平滑信息，以改善在存在偶然符合事件和衰减情况下的正电子发射断层扫描（PET）图像重建。代数重建技术（ART）应用于这种新估计，并证明了其收敛性。这种数值技术基于行运算。计算复杂度仅与像素大小和探测器管大小呈线性关系。因此，对于大型稀疏系统，它在存储和计算方面效率很高。此外，轻松纳入范围限制和空间可变惩罚不会降低效率。所有这些使得新方法在实际中适用。还研究了基于广义交叉验证的这种新估计的自动数据驱动选择方法。蒙特卡罗研究证明了这种新方法的优势。