Division of Human Health, IAEA, Vienna A1400, Austria.
Med Phys. 2010 Oct;37(10):5550-9. doi: 10.1118/1.3483784.
Respiratory gating in PET is an approach used to minimize the negative effects of respiratory motion on spatial resolution. It is based on an initial determination of a patient's respiratory movements during a scan, typically using hardware based systems. In recent years, several fully automated databased algorithms have been presented for extracting a respiratory signal directly from PET data, providing a very practical strategy for implementing gating in the clinic. In this work, a new method is presented for extracting a respiratory signal from raw PET sinogram data and compared to previously presented automated techniques.
The acquisition of respiratory signal from PET data in the newly proposed method is based on rebinning the sinogram data into smaller data structures and then analyzing the time activity behavior in the elements of these structures. From this analysis, a 1D respiratory trace is produced, analogous to a hardware derived respiratory trace. To assess the accuracy of this fully automated method, respiratory signal was extracted from a collection of 22 clinical FDG-PET scans using this method, and compared to signal derived from several other software based methods as well as a signal derived from a hardware system.
The method presented required approximately 9 min of processing time for each 10 min scan (using a single 2.67 GHz processor), which in theory can be accomplished while the scan is being acquired and therefore allowing a real-time respiratory signal acquisition. Using the mean correlation between the software based and hardware based respiratory traces, the optimal parameters were determined for the presented algorithm. The mean/median/range of correlations for the set of scans when using the optimal parameters was found to be 0.58/0.68/0.07-0.86. The speed of this method was within the range of real-time while the accuracy surpassed the most accurate of the previously presented algorithms.
PET data inherently contains information about patient motion; information that is not currently being utilized. We have shown that a respiratory signal can be extracted from raw PET data in potentially real-time and in a fully automated manner. This signal correlates well with hardware based signal for a large percentage of scans, and avoids the efforts and complications associated with hardware. The proposed method to extract a respiratory signal can be implemented on existing scanners and, if properly integrated, can be applied without changes to routine clinical procedures.
在 PET 中使用呼吸门控技术是一种用于最小化呼吸运动对空间分辨率的负面影响的方法。它基于在扫描过程中初始确定患者的呼吸运动，通常使用基于硬件的系统。近年来，已经提出了几种从 PET 数据中直接提取呼吸信号的全自动基于数据库的算法，为在临床中实施门控提供了一种非常实用的策略。在这项工作中，提出了一种从原始 PET 正弦图数据中提取呼吸信号的新方法，并与以前提出的自动技术进行了比较。
在新提出的方法中，从 PET 数据中获取呼吸信号是基于将正弦图数据重新划分为较小的数据结构，然后分析这些结构元素中的时间活动行为。从这个分析中，产生了一个类似于硬件衍生的呼吸轨迹的 1D 呼吸轨迹。为了评估这种完全自动方法的准确性，使用该方法从 22 例临床 FDG-PET 扫描中提取了呼吸信号，并与来自其他几种软件方法的信号以及来自硬件系统的信号进行了比较。
该方法对于每 10 分钟扫描需要大约 9 分钟的处理时间（使用单个 2.67 GHz 处理器），理论上可以在扫描采集的同时完成，从而允许实时采集呼吸信号。使用软件和硬件呼吸轨迹之间的平均相关性，确定了用于该算法的最佳参数。当使用最佳参数时，扫描集的平均/中位数/范围相关系数为 0.58/0.68/0.07-0.86。该方法的速度在实时范围内，准确性超过了以前提出的最准确的算法。
PET 数据本质上包含有关患者运动的信息；目前未被利用的信息。我们已经表明，可以从原始 PET 数据中以潜在实时和全自动的方式提取呼吸信号。该信号与大部分扫描的硬件信号相关性很好，并且避免了与硬件相关的努力和复杂性。从原始 PET 数据中提取呼吸信号的建议方法可以在现有的扫描仪上实现，如果正确集成，可以在不改变常规临床程序的情况下应用。
