Elfhakri Georges
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:4520-3. doi: 10.1109/EMBC.2015.7319399.
Traditional data-driven respiratory gating method is capable of detecting breathing cycles directly from positron emission tomography (PET) data, but usually fails at low SNR, particularly at low dose PET/CT study. Time-of-flight (TOF) PET has the potential to improve the SNR. In order for TOF information to reduce the statistical noise and boost the performance of respiratory gating, we present a robust data-driven respiratory gating method using TOF information, which retrospectively derived the respiratory signal from the acquired TOF-PET data. The PET data was acquired in list mode format and analyzed in sinogram space. The method was demonstrated with patient datasets acquired on a TOF PET/CT system. Data-driven gating methods by center of mass (COM) and principle component analysis (PCA) algorithm were successfully performed on nonTOF PET and TOF PET dataset. To assess the accuracy of the data-driven respiratory signal, a hardware-based signal was acquired for comparison. The study showed that retrospectively respiratory gating using TOF sinograms has improved the SNR, and outperforms the non-TOF gating under both COM and PCA algorithms.
传统的数据驱动型呼吸门控方法能够直接从正电子发射断层扫描（PET）数据中检测呼吸周期，但在低信噪比情况下通常会失效，尤其是在低剂量PET/CT研究中。飞行时间（TOF）PET有提高信噪比的潜力。为了使TOF信息减少统计噪声并提高呼吸门控的性能，我们提出了一种使用TOF信息的稳健的数据驱动型呼吸门控方法，该方法从采集到的TOF-PET数据中回顾性地推导呼吸信号。PET数据以列表模式格式采集，并在正弦图空间中进行分析。该方法在使用TOF PET/CT系统采集的患者数据集中得到了验证。基于质心（COM）和主成分分析（PCA）算法的数据驱动型门控方法在非TOF PET和TOF PET数据集上均成功实施。为了评估数据驱动型呼吸信号的准确性，采集了基于硬件的信号进行比较。研究表明，使用TOF正弦图进行回顾性呼吸门控提高了信噪比，并且在COM和PCA算法下均优于非TOF门控。
