IEEE Trans Med Imaging. 2017 Jan;36(1):203-213. doi: 10.1109/TMI.2016.2594150. Epub 2016 Aug 24.
Direct reconstruction of parametric images from raw photon counts has been shown to improve the quantitative analysis of dynamic positron emission tomography (PET) data. However it suffers from subject motion which is inevitable during the typical acquisition time of 1-2 hours. In this work we propose a framework to jointly estimate subject head motion and reconstruct the motion-corrected parametric images directly from raw PET data, so that the effects of distorted tissue-to-voxel mapping due to subject motion can be reduced in reconstructing the parametric images with motion-compensated attenuation correction and spatially aligned temporal PET data. The proposed approach is formulated within the maximum likelihood framework, and efficient solutions are derived for estimating subject motion and kinetic parameters from raw PET photon count data. Results from evaluations on simulated [C]raclopride data using the Zubal brain phantom and real clinical [F]florbetapir data of a patient with Alzheimer's disease show that the proposed joint direct parametric reconstruction motion correction approach can improve the accuracy of quantifying dynamic PET data with large subject motion.
从原始光子计数中直接重建参数图像已被证明可以改善动态正电子发射断层扫描（PET）数据的定量分析。然而，它受到在典型的 1-2 小时采集时间内不可避免的主体运动的影响。在这项工作中，我们提出了一个框架，从原始 PET 数据中联合估计主体头部运动并直接重建运动校正的参数图像，以便在使用运动补偿衰减校正和空间对齐的时间 PET 数据重建参数图像时，可以减少由于主体运动导致的组织到体素映射的失真。所提出的方法是在最大似然框架内制定的，并为从原始 PET 光子计数数据中估计主体运动和动力学参数推导出了有效的解决方案。使用 Zubal 脑模型对模拟 [C]raclopride 数据和一位阿尔茨海默病患者的真实临床 [F]florbetapir 数据进行评估的结果表明，所提出的联合直接参数重建运动校正方法可以提高大运动主体的动态 PET 数据定量的准确性。
