心脏 PET 中的身体运动检测和校正：体模和人体研究。
Body motion detection and correction in cardiac PET: Phantom and human studies.
机构信息
Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.
Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA.
出版信息
Med Phys. 2019 Nov;46(11):4898-4906. doi: 10.1002/mp.13815. Epub 2019 Oct 8.
PURPOSE
Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We propose and evaluate a novel method to detect, estimate, and correct body motion in cardiac PET.
METHODS
Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction. For motion detection, we first divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences' distribution in each bin. We then compute the covariance matrix within a 25-s sliding window over the COM signals inside the window. The sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into "static" (i.e., body motion free) and "moving" (i.e. contaminated by body motion) frames. Each moving frame is further divided into a number of evenly spaced sub-frames (referred to as "sub-moving" frames), in which motion is assumed to be negligible. For motion estimation, we first reconstruct the data in each static and sub-moving frame using a rapid back-projection technique. We then select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration. For motion-compensated image reconstruction, we reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix. We evaluated the performance of our approach in both phantom and human studies.
RESULTS
Visually, the motion-corrected (MC) PET images obtained using the proposed method have better quality and fewer motion artifacts than the images reconstructed without motion correction (NMC). Quantitative analysis indicates that MC yields higher myocardium to blood pool concentration ratios. MC also yields sharper myocardium than NMC.
CONCLUSIONS
The proposed body motion correction method improves image quality of cardiac PET.
目的
在心脏正电子发射断层扫描（PET）期间，患者身体运动会严重降低图像质量。我们提出并评估了一种新颖的方法，用于检测、估计和校正心脏 PET 中的身体运动。
方法
我们的方法由三个关键组件组成：运动检测、运动估计和运动补偿图像重建。对于运动检测，我们首先将 PET 列表模式数据分为 1 秒的 bin，并计算每个 bin 中符合事件分布的质心（COM）。然后，我们在窗口内的 COM 信号上计算 25 秒滑动窗口内的协方差矩阵。协方差矩阵的特征值之和用于将列表模式数据分为“静态”（即无身体运动）和“移动”（即受身体运动污染）帧。每个移动帧进一步分为多个均匀间隔的子帧（称为“子移动”帧），其中运动可以忽略不计。对于运动估计，我们首先使用快速反投影技术重建每个静态和子移动帧的数据。然后，我们选择最长的静态帧作为参考帧，并使用非刚性配准从所有其他静态和子移动帧中估计到参考帧的弹性运动变换。对于运动补偿图像重建，我们通过在 PET 系统矩阵中合并估计的运动变换，将所有列表模式数据重建到参考帧中的单个图像体积中。我们在体模和人体研究中评估了我们方法的性能。
结果
从视觉上看，与未进行运动校正（NMC）的图像相比，使用所提出的方法获得的运动校正（MC）PET 图像质量更好，运动伪影更少。定量分析表明，MC 可获得更高的心肌与血池浓度比。MC 还产生比 NMC 更清晰的心肌。
结论
所提出的身体运动校正方法可提高心脏 PET 的图像质量。
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