Acceleration of motion-compensated PET reconstruction: ordered subsets-gates EM algorithms and a priori reference gate information
Acceleration of motion-compensated PET reconstruction: ordered subsets-gates EM algorithms and a priori reference gate information
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DOI：
10.1088/0031-9155/56/6/011
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发表时间：
2011-03-21
影响因子：
3.5
通讯作者：
Fryer, T. D.
中科院分区：
文献类型：
--
作者：
Dikaios, N.;Fryer, T. D.
正电子发射断层扫描过程中的患者运动会导致显著的分辨率损失和图像退化。运动补偿图像重建（MCIR）算法已被证明是可靠的校正方法，精确的变形领域。然而，尽管有序子集（OS）被广泛用于加速收敛，OS-MCIR算法仍然是计算昂贵的。本研究集中在加速OS-MCIR算法通过两种方法：结合OS与运动子集和使用的初始估计的基础上参考门数据。将这些方法与两种现有OS-MCIR算法和使用NCAT体模数据的重建后配准进行比较。从噪声、病变偏倚和对比噪声比（CNR）方面对这些方法进行了评价。运动子集与投影子集的直接组合（OSGEM）产生比现有OSM-CIR算法差的结果（较低的CNR，p < 0.01）。使用来自所有门的数据向OSGEM添加间隔步骤导致算法（SS-OSGEM）以三分之一的计算费用生成与来自现有OS-MCIR算法的图像在统计学上一致的图像（CNR无显著差异，p > 0.05）。使用参考门极初始估计值（MCDOi）在计算负担减半的情况下，在偏倚和CNR（p > 0.05）方面的图像质量相当。这项研究表明，MCDOi和SS-OSGEM特别是有吸引力的加速OS-MCIR方法。
Patient motion during positron emission tomography scans leads to significant resolution loss and image degradation. Motion-compensated image reconstruction (MCIR) algorithms have proven to be reliable correction methods given accurate deformation fields. However, although ordered subsets (OS) are widely used to speed up the convergence, OS-MCIR algorithms are still computationally expensive. This study concentrates on acceleration of OS-MCIR algorithms through two methods: combining OS with motion subsets and use of an initial estimate based on reference gate data. These approaches were compared to two existing OS-MCIR algorithms and post-reconstruction registration using data from the NCAT phantom. The methods were evaluated in terms of noise, lesion bias and contrast-to-noise ratio (CNR). The straightforward combination of motion subsets with projection subsets (OSGEM) produced inferior results (lower CNR, p < 0.01) to existing OSM-CIR algorithms. The addition of a spacer step using data from all gates to OSGEM resulted in an algorithm (SS-OSGEM) that generated images that were statistically consistent with those from existing OS-MCIR algorithms (no significant difference in CNR, p > 0.05) at one third of the computational expense. The use of a reference gate initial estimate (MCDOi) resulted in comparable image quality in terms of bias and CNR (p > 0.05) at half the computational burden. This study indicates that MCDOi and SS-OSGEM in particular are attractive accelerated OS-MCIR approaches.