Ren Silin, Jin Xiao, Chan Chung, Jian Yiqiang, Mulnix Tim, Liu Chi, Carson Richard E
Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America.
Phys Med Biol. 2017 Jun 21;62(12):4741-4755. doi: 10.1088/1361-6560/aa700c. Epub 2017 May 18.
Data-driven respiratory gating techniques were developed to correct for respiratory motion in PET studies, without the help of external motion tracking systems. Due to the greatly increased image noise in gated reconstructions, it is desirable to develop a data-driven event-by-event respiratory motion correction method. In this study, using the Centroid-of-distribution (COD) algorithm, we established a data-driven event-by-event respiratory motion correction technique using TOF PET list-mode data, and investigated its performance by comparing with an external system-based correction method. Ten human scans with the pancreatic β-cell tracer F-FP-(+)-DTBZ were employed. Data-driven respiratory motions in superior-inferior (SI) and anterior-posterior (AP) directions were first determined by computing the centroid of all radioactive events during each short time frame with further processing. The Anzai belt system was employed to record respiratory motion in all studies. COD traces in both SI and AP directions were first compared with Anzai traces by computing the Pearson correlation coefficients. Then, respiratory gated reconstructions based on either COD or Anzai traces were performed to evaluate their relative performance in capturing respiratory motion. Finally, based on correlations of displacements of organ locations in all directions and COD information, continuous 3D internal organ motion in SI and AP directions was calculated based on COD traces to guide event-by-event respiratory motion correction in the MOLAR reconstruction framework. Continuous respiratory correction results based on COD were compared with that based on Anzai, and without motion correction. Data-driven COD traces showed a good correlation with Anzai in both SI and AP directions for the majority of studies, with correlation coefficients ranging from 63% to 89%. Based on the determined respiratory displacements of pancreas between end-expiration and end-inspiration from gated reconstructions, there was no significant difference between COD-based and Anzai-based methods. Finally, data-driven COD-based event-by-event respiratory motion correction yielded comparable results to that based on Anzai respiratory traces, in terms of contrast recovery and reduced motion-induced blur. Data-driven event-by-event respiratory motion correction using COD showed significant image quality improvement compared with reconstructions with no motion correction, and gave comparable results to the Anzai-based method.
数据驱动的呼吸门控技术旨在在不借助外部运动跟踪系统的情况下校正PET研究中的呼吸运动。由于门控重建中图像噪声大幅增加，因此需要开发一种数据驱动的逐事件呼吸运动校正方法。在本研究中，我们使用分布质心（COD）算法，建立了一种利用TOF PET列表模式数据的数据驱动逐事件呼吸运动校正技术，并通过与基于外部系统的校正方法进行比较来研究其性能。我们使用了10例注射胰腺β细胞示踪剂F-FP-(+)-DTBZ的人体扫描数据。首先通过计算每个短时间帧内所有放射性事件的质心并进行进一步处理，确定上下（SI）和前后（AP）方向的数据驱动呼吸运动。在所有研究中均采用安齐腰带系统记录呼吸运动。首先通过计算皮尔逊相关系数，将SI和AP方向的COD轨迹与安齐轨迹进行比较。然后，基于COD或安齐轨迹进行呼吸门控重建，以评估它们在捕捉呼吸运动方面的相对性能。最后，基于所有方向上器官位置位移与COD信息的相关性，根据COD轨迹计算SI和AP方向上连续的3D内部器官运动，以指导MOLAR重建框架中的逐事件呼吸运动校正。将基于COD的连续呼吸校正结果与基于安齐的结果以及无运动校正的结果进行比较。在大多数研究中，数据驱动的COD轨迹在SI和AP方向上与安齐轨迹均具有良好的相关性，相关系数范围为63%至89%。根据门控重建确定的呼气末和吸气末之间胰腺的呼吸位移，基于COD的方法和基于安齐的方法之间没有显著差异。最后，就对比度恢复和减少运动引起的模糊而言，基于数据驱动的COD的逐事件呼吸运动校正产生的结果与基于安齐呼吸轨迹的结果相当。与无运动校正的重建相比，使用COD进行的数据驱动逐事件呼吸运动校正显示出图像质量有显著提高，并且与基于安齐的方法产生的结果相当。
