MR-Guided Kernel EM Reconstruction for Reduced Dose PET Imaging.
MR-Guided Kernel EM Reconstruction for Reduced Dose PET Imaging.
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DOI：
10.1109/trpms.2017.2771490
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发表时间：
2018-05
影响因子：
4.4
通讯作者：
Reader AJ
中科院分区：
文献类型：
--
作者：
Bland J;Mehranian A;Belzunce MA;Ellis S;McGinnity CJ;Hammers A;Reader AJ
PET 图像重建非常容易受到泊松噪声的影响，如果使用更短的采集时间或减少注射剂量，则嘈杂的 PET 数据变得更加有限。最近发展的核期望最大化（KEM）是一种减少 PET 图像噪声的简单方法，我们在这项工作中表明，当核方法与 MR 衍生核一起使用时，可以实现令人印象深刻的剂量减少。对于低计数数据集（对应于在减少注射剂量下获得的数据集）的重建，核方法被证明超越了最大似然期望最大化（MLEM），在所有计数水平上为未平滑和平滑的图像产生明显更清晰的重建。 10% 数据的内核 EM 重建具有与 100% 数据的 MLEM 重建相当的全脑体素级误差测量（对于模拟数据，在 100 次迭代时）。对于区域指标，与 MLEM 相比，降低剂量水平下的核方法获得了更低的变异系数和更准确的平均值。然而，核方法所带来的进步是以 PET 数据特有的特征可能过度平滑为代价的。需要对临床数据进行进一步评估，以确定使用核方法常规可以实现的剂量减少水平，同时保持扫描的诊断效用。
PET image reconstruction is highly susceptible to the impact of Poisson noise, and if shorter acquisition times or reduced injected doses are used, the noisy PET data become even more limiting. The recent development of kernel expectation maximisation (KEM) is a simple way to reduce noise in PET images, and we show in this work that impressive dose reduction can be achieved when the kernel method is used with MR-derived kernels. The kernel method is shown to surpass maximum likelihood expectation maximisation (MLEM) for the reconstruction of low-count datasets (corresponding to those obtained at reduced injected doses) producing visibly clearer reconstructions for unsmoothed and smoothed images, at all count levels. The kernel EM reconstruction of 10% of the data had comparable whole brain voxel-level error measures to the MLEM reconstruction of 100% of the data (for simulated data, at 100 iterations). For regional metrics, the kernel method at reduced dose levels attained a reduced coefficient of variation and more accurate mean values compared to MLEM. However, the advances provided by the kernel method are at the expense of possible over-smoothing of features unique to the PET data. Further assessment on clinical data is required to determine the level of dose reduction that can be routinely achieved using the kernel method, whilst maintaining the diagnostic utility of the scan.