IEEE Trans Med Imaging. 2018 Mar;37(3):703-711. doi: 10.1109/TMI.2017.2768130.
The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, F-FDG and Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions.
研究不同正电子发射断层扫描（PET）重建和运动补偿方法的性能需要准确、真实地再现采集过程中在实际对象中观察到的解剖结构和运动轨迹。由于存在许多不同的临床方案、扫描仪规格、患者体型和生理变化，因此很难生成良好控制的临床数据集。相反，可以使用计算体模来为不同的疾病状态生成大型数据集，提供真实情况。一些研究使用动态图像的配准来推导出体素变形，以创建运动计算体模。这些体模与仿真软件一起生成原始数据。本文提出了一种使用快速解析方法合成动态 PET 数据的方法。通过将呼吸运动的真实模型纳入数值体模中，使用基于磁共振成像（MRI）的运动建模和高分辨率 MRI 图像来生成具有连续和可变运动的数据集，从而实现了这一点。本文介绍了两个不同临床轨迹的数据集，即 F-FDG 和 Ga-PSMA。该方法结合了呼吸运动的真实模型，以生成时间和空间相关的 MRI 和 PET 数据集，这些数据集有望从同时进行的 PET-MRI 采集获得。
