Gordon Center for Medical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School Boston MA 02114 United States of America. Contributed equally to this work.
Phys Med Biol. 2020 Aug 19;65(16):165007. doi: 10.1088/1361-6560/ab9660.
It is important to measure the respiratory cycle in positron emission tomography (PET) to enhance the contrast of the tumor as well as the accuracy of its localization in organs such as the lung and liver. Several types of data-driven respiratory gating methods, such as center of mass and principal component analysis, have been developed to directly measure the breathing cycle from PET images and listmode data. However, the breathing cycle is still hard to detect in low signal-to-noise ratio (SNR) data, particularly in low dose PET/CT scans. To address this issue, a time-of-flight (TOF) PET is currently utilized for the data-driven respiratory gating because of its higher SNR and better localization of the region of interest. To further improve the accuracy of respiratory gating with TOF information, we propose an accurate data-driven respiratory gating method, which retrospectively derives the respiratory signal using a localized sensing method based on a diaphragm mask in TOF PET data. To assess the accuracy of the proposed method, the performance is evaluated with three patient datasets, and a pressure-belt signal as the ground truth is compared. In our experiments, we validate that the respiratory signal using the proposed data-driven gating method is well matched to the pressure-belt respiratory signal with less than 5% peak time errors and over 80% trace correlations. Based on gated signals, the respiratory-gated image of the proposed method provides more clear edges of organs compared to images using conventional non-TOF methods. Therefore, we demonstrate that the proposed method can achieve improvements for the accuracy of gating signals and image quality.
在正电子发射断层扫描（PET）中测量呼吸周期对于提高肿瘤对比度以及提高肺和肝等器官的定位准确性非常重要。已经开发了几种数据驱动的呼吸门控方法，例如质心和主成分分析，以直接从 PET 图像和列表模式数据中测量呼吸周期。然而，在低信噪比（SNR）数据中，特别是在低剂量 PET/CT 扫描中，仍然很难检测到呼吸周期。为了解决这个问题，目前使用飞行时间（TOF）PET 进行数据驱动的呼吸门控，因为它具有更高的 SNR 和更好的感兴趣区域定位。为了进一步提高基于 TOF 信息的呼吸门控的准确性，我们提出了一种准确的数据驱动的呼吸门控方法，该方法使用基于 TOF PET 数据中膈肌面罩的局部感应方法来回顾性地获得呼吸信号。为了评估该方法的准确性，使用三个患者数据集对其性能进行了评估，并与压力带信号作为基准进行了比较。在我们的实验中，我们验证了使用所提出的数据驱动门控方法获得的呼吸信号与压力带呼吸信号非常匹配，峰值时间误差小于 5%，迹线相关性超过 80%。基于门控信号，与使用传统非 TOF 方法相比，所提出方法的呼吸门控图像提供了更清晰的器官边缘。因此，我们证明了该方法可以提高门控信号和图像质量的准确性。
