Quantitative dynamic cardiac 82Rb PET using generalized factor and compartment analyses.
Quantitative dynamic cardiac 82Rb PET using generalized factor and compartment analyses.
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发表时间：
2005-08
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通讯作者：
G. El Fakhri;Arkadiusz Sitek;B. Guérin;M. Kijewski;M. D. Di Carli;S. Moore
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作者：
G. El Fakhri;Arkadiusz Sitek;B. Guérin;M. Kijewski;M. D. Di Carli;S. Moore
未标记我们已经解决了（82）Rb心脏PET的2个主要挑战，准确输入函数的无创估计和心肌灌注的绝对定量，使用动态序列的最小二乘因子分析（GFADS）和一种新的房室分析方法的广义形式。方法：从10个静息/负荷试验中生成左、右心室（LV + RV）时间-活动曲线（TAC），并对30个心肌TAC进行建模，以涵盖一系列临床值。分别生成LV、RV、心肌和其他器官的二维PET Monte Carlo模拟，并使用上述TAC进行组合，以形成30项真实动态（82）Rb研究。通过GFADS估计LV和RV TAC，并用作2室动力学分析的输入，该分析通过正交体素分组估计心肌组织提取（k（1））和流出（k（2））以及LV + RV贡献（f（v），r（v））的参数图。此外，13名患者注射了2.22 +/- 0.19 GBq（60 +/- 5 mCi）的（82）Rb，并在休息和潘生丁应激期间动态成像6分钟。结果在蒙特卡罗模拟中，GFADS得到了3个因子的估计值和相应的因子图像，LV、RV和心肌因子估计值的平均误差分别为-4.2% +/-6.3%，3.5% +/-4.3%和2.0% +/- 5.5%。与使用基于手动绘制的感兴趣体积的TAC获得的估计值相比，该估计值显着更准确且对噪声更鲁棒（P < 0.01）。二房室方法产生了准确的k（1）、k（2）、f（v）和r（v）参数图; k（1）估计值的平均误差为6.8% ± 3.6%。在所有患者研究中，我们的方法得到了k（1）、k（2）、f（v）和r（v）的稳健估计值，这些估计值与受试者的状态和导管插入结果非常相关。结论采用动态序列的广义因子分析和房室模型定量动态（82）Rb PET可估计绝对心肌灌注和动力学参数，误差<9%。
UNLABELLED We have addressed 2 major challenges of (82)Rb cardiac PET, noninvasive estimation of an accurate input function and absolute quantitation of myocardial perfusion, using a generalized form of least-squares factor analysis of dynamic sequences (GFADS) and a novel compartment analysis approach. METHODS Left and right ventricular (LV + RV) time-activity curves (TACs) were generated from 10 rest/stress studies, and 30 myocardial TACs were modeled to cover a range of clinical values. Two-dimensional PET Monte Carlo simulations of the LV, RV, myocardium, and other organs were generated separately and combined using the above TACs to form 30 realistic dynamic (82)Rb studies. LV and RV TACs were estimated by GFADS and used as input to a 2-compartment kinetic analysis that estimates parametric maps of myocardial tissue extraction (k(1)) and egress (k(2)), as well as LV + RV contributions (f(v), r(v)), by orthogonal voxel grouping. In addition, 13 patients were injected with 2.22 +/- 0.19 GBq (60 +/- 5 mCi) of (82)Rb and imaged dynamically for 6 min at rest and during dipyridamole stress. RESULTS In Monte Carlo simulations, GFADS yielded estimates of the 3 factors and corresponding factor images, with average errors of -4.2% +/- 6.3%, 3.5% +/- 4.3%, and 2.0% +/- 5.5% in the LV, RV, and myocardial factor estimates, respectively. The estimates were significantly more accurate and robust to noise than those obtained using TACs based on manually drawn volumes of interest (P < 0.01). The 2-compartment approach yielded accurate k(1), k(2), f(v), and r(v) parametric maps; the average error of estimates of k(1) was 6.8% +/- 3.6%. In all patient studies, our approach yielded robust estimates of k(1), k(2), f(v), and r(v), which correlated very well with the status of the subject and the catheterization results. CONCLUSION Quantitative dynamic (82)Rb PET using generalized factor analysis of dynamic sequences and compartmental modeling yields estimates of parameters of absolute myocardial perfusion and kinetics with errors of <9%.