Clinical Imaging Research Centre, A*STAR-NUS, Singapore
CERMEP-Imagerie du Vivant, Lyon, France.
J Nucl Med. 2018 Nov;59(11):1761-1767. doi: 10.2967/jnumed.117.206375. Epub 2018 Apr 13.
Head motion occurring during brain PET studies leads to image blurring and to bias in measured local quantities. The objective of this work was to implement a correction method for PET data acquired with the mMR synchronous PET/MR scanner. A list-mode-based motion-correction approach has been designed. The developed rebinner chronologically reads the recorded events from the Siemens list-mode file, applies the estimated geometric transformations, and frames the detected counts into sinograms. The rigid-body motion parameters were estimated from an initial dynamic reconstruction of the PET data. We then optimized the correction for C-Pittsburgh compound B (C-PIB) scans using simulated and actual data with well-controlled motion. An efficient list-mode-based motion correction approach has been implemented, fully optimized, and validated using simulated and actual PET data. The average spatial resolution loss induced by inaccuracies in motion parameter estimates and by the rebinning process was estimated to correspond to a 1-mm increase in full width at half maximum with motion parameters estimated directly from the PET data with a temporal frequency of 20 s. The results show that the rebinner can be safely applied to the C-PIB scans, allowing almost complete removal of motion-induced artifacts. The application of the correction method to a large cohort of C-PIB scans led to the following observations: first, that more than 21% of the scans were affected by motion greater than 10 mm (39% for subjects with Mini-Mental State Examination scores below 20), and second, that the correction led to quantitative changes in Alzheimer-specific cortical regions of up to 30%. The rebinner allows accurate motion correction at a cost of minimal resolution reduction. Application of the correction to a large cohort of C-PIB scans confirmed the necessity of systematically correcting for motion to obtain quantitative results.
头部运动在脑 PET 研究中会导致图像模糊和测量局部量的偏差。本工作的目的是为 mMR 同步 PET/MR 扫描仪采集的 PET 数据实现一种校正方法。设计了一种基于列表模式的运动校正方法。所开发的重排器按时间顺序从西门子列表模式文件中读取记录的事件,应用估计的几何变换,并将检测到的计数排列成正弦图。刚体运动参数是从 PET 数据的初始动态重建中估计的。然后,我们使用模拟和实际具有良好控制运动的数据优化了 C-Pittsburgh 复合 B(C-PIB)扫描的校正。已经实现了一种有效的基于列表模式的运动校正方法,该方法已完全优化并使用模拟和实际 PET 数据进行了验证。通过对运动参数估计和重排过程中的不准确性进行模拟,估计平均空间分辨率损失相当于在最大半高全宽处增加 1 毫米,而运动参数则直接从 PET 数据中以 20 秒的时间频率估计。结果表明,重排器可以安全地应用于 C-PIB 扫描,几乎可以完全消除运动引起的伪影。该校正方法在大量 C-PIB 扫描中的应用得到了以下观察结果:首先,超过 21%的扫描受到大于 10 毫米的运动影响(对于 Mini-Mental State Examination 得分低于 20 的受试者,这一比例为 39%),其次,校正导致阿尔茨海默病特异性皮质区域的定量变化高达 30%。重排器允许以最小分辨率降低为代价进行准确的运动校正。该校正方法在大量 C-PIB 扫描中的应用证实了为获得定量结果有必要系统地校正运动。