Analysis and comparison of two methods for motion correction in PET imaging
Analysis and comparison of two methods for motion correction in PET imaging
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DOI：
10.1118/1.4754586
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发表时间：
2012-10-01
期刊：
影响因子：
3.8
通讯作者：
Marsden, P. K.
中科院分区：
文献类型：
--
作者：
Polycarpou, I.;Tsoumpas, C.;Marsden, P. K.
目的：虽然已经提出了各种正电子发射断层扫描（PET）运动校正的方法，但没有足够的证据来回答哪种方法在实践中更好。这项调查的目的是表征两个主要的运动校正approaches在收敛和图像properties.Methods方面的行为：对于第一种方法，重建-变换-平均（RTA），每个门的重建被转换为一个参考门和平均。在第二种方法，运动补偿图像重建（MCIR），运动信息被纳入重建。这两种技术研究的基础上有序子集期望最大化算法。从对人类志愿者进行的动态MR采集中获得运动信息，并从动态MR数据中模拟并行PET数据。这两种方法进行了评估，使用多个实现，以准确地定义重建images.Results的噪声属性的统计：MCIR成功地恢复了所有区域的真实值，而RTA由于有限的计数统计和插值误差在变换步骤中有很高的偏差。此外，RTA噪声非常小且稳定，而在MCIR中，噪声随着迭代次数而逐渐变大，因此仅当噪声被处理时，MCIR在MSE方面优于RTA。例如，MCIR与后置滤波的结果在MSE高达42%低于RTA.Conclusions：这项研究表明，MCIR可以提供上级性能整体RTA，如果噪声最小化。然而，在量化不是主要目标的应用中，RTA可以是校正运动的实用且简单的方法。(C)2012年美国医学物理学家协会。[http：//dx.doi.org/10.1118/1.4754586]
Purpose: Although there have been various proposed methods for positron emission tomography (PET) motion correction, there is not sufficient evidence to answer which method is better in practice. This investigation aims to characterize the behavior of the two main motion-correction approaches in terms of convergence and image properties.Methods: For the first method, reconstruct-transform-average (RTA), reconstructions of each gate are transformed to a reference gate and averaged. In the second method, motion-compensated image reconstruction (MCIR), motion information is incorporated within the reconstruction. Both techniques studied were based on the ordered subsets expectation maximization algorithm. Motion information was obtained from a dynamic MR acquisition performed on a human volunteer and concurrent PET data were simulated from the dynamic MR data. The two approaches were assessed statistically using multiple realizations to accurately define the noise properties of the reconstructed images.Results: MCIR successfully recovers the true values of all regions, whereas RTA has high bias due to the limited count-statistics and interpolation errors during the transformation step. In addition, RTA noise is very small and stabilized, whereas in MCIR noise becomes progressively greater with the number of iterations and therefore MCIR outperforms RTA in terms of MSE only if noise is treated. For example, MCIR with postfiltering results in MSE up to 42% lower than RTA.Conclusions: This study indicates that MCIR may provide superior performance overall to RTA if noise is minimized. However, in applications where quantification is not the main objective RTA can be a practical and simple method to correct for motion. (C) 2012 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4754586]