Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET.
Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET.
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DOI：
10.1109/tmi.2017.2761756
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发表时间：
2018-03
影响因子：
10.6
通讯作者：
Liu C
中科院分区：
文献类型：
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作者：
Chan C;Onofrey J;Jian Y;Germino M;Papademetris X;Carson RE;Liu C
在静态和动态研究中，PET/CT成像过程中的呼吸运动会导致严重的图像模糊和低估示踪剂浓度。为了消除周期内和周期间的运动，并将其应用于动态成像，我们提出了一种非刚性逐个事件(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，第一部分利用内外运动相关(NR-INTEX)估计内部器官的连续非刚性运动场。然后，将该连续运动场合并到第二分量非刚性磨牙(NR-Molar)重建算法中，以将系统矩阵变形到获取衰减CT的参考位置。NR-Molar中的点扩展函数(PSF)和飞行时间(TOF)核被包含在系统矩阵计算中，因此也随着运动而变形。我们首先使用模拟呼吸运动的xCAT体模验证了NR-Molar。然后，使用这两个组件的非刚性EBE运动补偿图像重建在三个注射了18F-FPDTBZ的人体研究和一个注射了18F-FDG示踪剂的人体研究中得到了验证。将人类的结果与传统的使用离散运动场的非刚性运动校正(NR-离散，每个门一个运动场)和先前提出的刚性EBE运动补偿图像重建(R-EBE)进行比较，R-EBE旨在校正目标病变/器官上的刚性运动。XCAT结果表明，结合了PSF和TOF核的NR磨牙有效地校正了非刚性运动。18F-FPDTBZ研究表明，NR-EBE优于NR-离散，在靶器官上获得与R-EBE相似的结果，而在其他区域获得更好的图像质量。FDG研究表明，NR-EBE明显改善了肝脏中多个移动性病变的可见性，其中一些在其他重建中无法辨别，此外还改善了量化。这些结果表明，在使用PET成像胸腹部区域时，NR-EBE运动补偿图像重建是一种很有前途的病变检测和量化工具。
Respiratory motion during PET/CT imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this study, 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 2 components, the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation (NR-INTEX). 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. Non-rigid EBE motion compensated image reconstruction using both components were then validated on three human studies injected with 18F-FPDTBZ and one with 18F-FDG tracers. The human results were compared to 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 18F-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.