IEEE Trans Med Imaging. 2018 Feb;37(2):504-515. doi: 10.1109/TMI.2017.2761756. Epub 2017 Oct 10.
Respiratory motion during positron emission tomography (PET)/computed tomography (CT) imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this paper, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-by-event (NR-EBE) respiratory motion-compensated list-mode reconstruction algorithm. The proposed method consists of two components: the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation. This continuous motion field is then incorporated into the second component, non-rigid MOLAR (NR-MOLAR) reconstruction algorithm to deform the system matrix to the reference location where the attenuation CT is acquired. The point spread function (PSF) and time-of-flight (TOF) kernels in NR-MOLAR are incorporated in the system matrix calculation, and therefore are also deformed according to motion. We first validated NR-MOLAR using a XCAT phantom with a simulated respiratory motion. NR-EBE motion-compensated image reconstruction using both the components was then validated on three human studies injected with F-FPDTBZ and one with F-fluorodeoxyglucose (FDG) tracers. The human results were compared with conventional non-rigid motion correction using discrete motion field (NR-discrete, one motion field per gate) and a previously proposed rigid EBE motion-compensated image reconstruction (R-EBE) that was designed to correct for rigid motion on a target lesion/organ. The XCAT results demonstrated that NR-MOLAR incorporating both PSF and TOF kernels effectively corrected for non-rigid motion. The F-FPDTBZ studies showed that NR-EBE out-performed NR-Discrete, and yielded comparable results with R-EBE on target organs while yielding superior image quality in other regions. The FDG study showed that NR-EBE clearly improved the visibility of multiple moving lesions in the liver where some of them could not be discerned in other reconstructions, in addition to improving quantification. These results show that NR-EBE motion-compensated image reconstruction appears to be a promising tool for lesion detection and quantification when imaging thoracic and abdominal regions using PET.
正电子发射断层扫描（PET）/计算机断层扫描（CT）成像过程中的呼吸运动会导致静态和动态研究中的图像模糊和示踪剂浓度低估。在本文中，我们旨在消除周期内和周期间运动，并将其应用于动态成像，开发了一种非刚性事件驱动（NR-EBE）呼吸运动补偿列表模式重建算法。该方法由两部分组成：第一部分使用内外运动相关性估计内部器官的连续非刚性运动场。然后，将此连续运动场合并到第二部分，即非刚性 MOLAR（NR-MOLAR）重建算法中，以将系统矩阵变形到采集衰减 CT 的参考位置。NR-MOLAR 中的点扩散函数（PSF）和飞行时间（TOF）核在系统矩阵计算中被合并，因此也根据运动而变形。我们首先使用具有模拟呼吸运动的 XCAT 体模验证了 NR-MOLAR。然后，在注射 F-FPDTBZ 的三个人体研究和一个注射 F-氟脱氧葡萄糖（FDG）示踪剂的人体研究上验证了这两个组件的 NR-EBE 运动补偿图像重建。将人体结果与使用离散运动场（NR-discrete，每个门一个运动场）的传统非刚性运动校正和以前提出的用于校正靶病变/器官刚性运动的刚性 EBE 运动补偿图像重建（R-EBE）进行了比较。XCAT 结果表明，合并 PSF 和 TOF 核的 NR-MOLAR 有效地校正了非刚性运动。F-FPDTBZ 研究表明，NR-EBE 优于 NR-Discrete，在靶器官上的结果与 R-EBE 相当，而在其他区域则具有更好的图像质量。FDG 研究表明，NR-EBE 明显改善了肝脏中多个运动病变的可见度，在其他重建中无法识别其中一些病变，除了改善定量外。这些结果表明，NR-EBE 运动补偿图像重建似乎是使用 PET 成像胸部和腹部区域时进行病变检测和定量的有前途的工具。
