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首页> 外文期刊>Physics in medicine and biology. >Acceleration of motion-compensated PET reconstruction: ordered subsets-gates EM algorithms and a priori reference gate information.
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Acceleration of motion-compensated PET reconstruction: ordered subsets-gates EM algorithms and a priori reference gate information.

机译：运动补偿PET重建的加速：有序子集门EM算法和先验参考门信息。

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摘要

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 OS-MCIR 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.
机译：正电子发射断层扫描中的患者运动会导致明显的分辨率损失和图像质量下降。给定精确的变形场，运动补偿图像重建（MCIR）算法已被证明是可靠的校正方法。但是，尽管广泛使用有序子集（OS）来加快收敛速度​​，但是OS-MCIR算法的计算量仍然很大。这项研究集中于通过两种方法来加速OS-MCIR算法：将OS与运动子集相结合以及基于参考门数据使用初始估计。将这些方法与两个现有的OS-MCIR算法和使用NCAT幻象的数据进行重建后配准进行了比较。评估了这些方法的噪声，病变偏度和对比噪声比（CNR）。运动子集与投影子集（OSGEM）的直接组合产生的效果较现有OS-MCIR算法差（CNR较低，p <0.01）。使用来自所有门的数据添加到OSGEM的间隔步骤增加了一种算法（SS-OSGEM），该算法生成的图像与现有OS-MCIR算法的图像统计上一致（CNR无显着差异，p> 0.05）计算费用的三分之一。参考门初始估计（MCDOi）的使用可在偏差和CNR（p> 0.05）方面获得可比的图像质量，而计算量仅为一半。这项研究表明，MCDOi和SS-OSGEM特别是有吸引力的加速OS-MCIR方法。

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