Yu Yunhan, Chan Chung, Ma Tianyu, Liu Yaqiang, Gallezot Jean-Dominique, Naganawa Mika, Kelada Olivia J, Germino Mary, Sinusas Albert J, Carson Richard E, Liu Chi
Department of Diagnostic Radiology, Yale University, New Haven, Connecticut Department of Engineering Physics, Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China.
Department of Diagnostic Radiology, Yale University, New Haven, Connecticut.
J Nucl Med. 2016 Jul;57(7):1084-90. doi: 10.2967/jnumed.115.167676. Epub 2016 Feb 23.
Existing respiratory motion-correction methods are applied only to static PET imaging. We have previously developed an event-by-event respiratory motion-correction method with correlations between internal organ motion and external respiratory signals (INTEX). This method is uniquely appropriate for dynamic imaging because it corrects motion for each time point. In this study, we applied INTEX to human dynamic PET studies with various tracers and investigated the impact on kinetic parameter estimation.
The use of 3 tracers-a myocardial perfusion tracer, (82)Rb (n = 7); a pancreatic β-cell tracer, (18)F-FP(+)DTBZ (n = 4); and a tumor hypoxia tracer, (18)F-fluoromisonidazole ((18)F-FMISO) (n = 1)-was investigated in a study of 12 human subjects. Both rest and stress studies were performed for (82)Rb. The Anzai belt system was used to record respiratory motion. Three-dimensional internal organ motion in high temporal resolution was calculated by INTEX to guide event-by-event respiratory motion correction of target organs in each dynamic frame. Time-activity curves of regions of interest drawn based on end-expiration PET images were obtained. For (82)Rb studies, K1 was obtained with a 1-tissue model using a left-ventricle input function. Rest-stress myocardial blood flow (MBF) and coronary flow reserve (CFR) were determined. For (18)F-FP(+)DTBZ studies, the total volume of distribution was estimated with arterial input functions using the multilinear analysis 1 method. For the (18)F-FMISO study, the net uptake rate Ki was obtained with a 2-tissue irreversible model using a left-ventricle input function. All parameters were compared with the values derived without motion correction.
With INTEX, K1 and MBF increased by 10% ± 12% and 15% ± 19%, respectively, for (82)Rb stress studies. CFR increased by 19% ± 21%. For studies with motion amplitudes greater than 8 mm (n = 3), K1, MBF, and CFR increased by 20% ± 12%, 30% ± 20%, and 34% ± 23%, respectively. For (82)Rb rest studies, INTEX had minimal effect on parameter estimation. The total volume of distribution of (18)F-FP(+)DTBZ and Ki of (18)F-FMISO increased by 17% ± 6% and 20%, respectively.
Respiratory motion can have a substantial impact on dynamic PET in the thorax and abdomen. The INTEX method using continuous external motion data substantially changed parameters in kinetic modeling. More accurate estimation is expected with INTEX.
现有的呼吸运动校正方法仅应用于静态PET成像。我们之前开发了一种逐事件呼吸运动校正方法，该方法利用内部器官运动与外部呼吸信号之间的相关性（INTEX）。这种方法特别适用于动态成像，因为它能对每个时间点的运动进行校正。在本研究中，我们将INTEX应用于使用各种示踪剂的人体动态PET研究，并研究其对动力学参数估计的影响。
在一项针对12名人体受试者的研究中，研究了3种示踪剂的使用——一种心肌灌注示踪剂，（82）Rb（n = 7）；一种胰腺β细胞示踪剂，（18）F - FP（+）DTBZ（n = 4）；以及一种肿瘤乏氧示踪剂，（18）F - 氟米索硝唑（（18）F - FMISO）（n = 1）。对（82）Rb进行了静息和负荷研究。使用安齐腰带系统记录呼吸运动。通过INTEX计算高时间分辨率下的三维内部器官运动，以指导每个动态帧中目标器官的逐事件呼吸运动校正。获取基于呼气末PET图像绘制的感兴趣区域的时间 - 活度曲线。对于（82）Rb研究，使用单组织模型并采用左心室输入函数获得K1。测定静息 - 负荷心肌血流量（MBF）和冠状动脉血流储备（CFR）。对于（18）F - FP（+）DTBZ研究，使用多线性分析1方法并采用动脉输入函数估计分布总体积。对于（18）F - FMISO研究，使用双组织不可逆模型并采用左心室输入函数获得净摄取率Ki。将所有参数与未进行运动校正时得出的值进行比较。
对于（82）Rb负荷研究，使用INTEX时，K1和MBF分别增加了10% ± 12%和15% ± 19%。CFR增加了19% ± 21%。对于运动幅度大于8 mm的研究（n = 3），K1、MBF和CFR分别增加了20% ± 12%、30% ± 20%和34% ± 23%。对于（82）Rb静息研究，INTEX对参数估计的影响最小。（18）F - FP（+）DTBZ的分布总体积和（18）F - FMISO的Ki分别增加了17% ± 6%和20%。
呼吸运动可对胸部和腹部的动态PET产生重大影响。使用连续外部运动数据的INTEX方法在动力学建模中显著改变了参数。预计使用INTEX可进行更准确的估计。
