Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application.
Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application.
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DOI：
10.1088/0031-9155/58/20/7391
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发表时间：
2013-10-21
影响因子：
3.5
通讯作者：
Rahmim A
中科院分区：
文献类型：
--
作者：
Karakatsanis NA;Lodge MA;Tahari AK;Zhou Y;Wahl RL;Rahmim A
采用标准化摄取值（SUV）的静态全身PET/CT被认为是广泛的肿瘤恶性肿瘤诊断和治疗反应监测的标准临床方法。在研究环境中已经实施了涉及动态获取时间图像的替代PET协议，允许量化示踪剂动力学，这是肿瘤表征和治疗反应监测的重要能力。尽管如此，动态方案仅限于单床覆盖，轴向视野限制在~ 15-20 cm，并且尚未转化为常规临床背景的全身PET成像检查弥散性疾病。在这里，我们通过在统一的框架内提出临床可行的多床动态PET采集协议和参数化成像方法，追求向动态全身PET参数化成像的过渡。我们研究了解决以下挑战的解决方案：(i)长时间采集，（ii）每床动态帧数少，以及（iii）血浆动力学的非侵入性量化。在本研究中，提出了一种新的动态（4D）全身PET采集方案，总长度约为45分钟，由(i)对心脏进行初始6分钟动态PET扫描（24帧），然后（ii）一系列多通道多床PET扫描（6次x 7个床位，每次扫描45秒）组成。采用标准的Patlak线性图形分析建模，结合图像衍生的等离子体输入函数测量。采用普通最小二乘（OLS） Patlak估计作为基线回归方法，以个体体素为基础量化示踪剂摄取率Ki和总血液分布体积V的生理参数。广泛的蒙特卡罗模拟研究，使用广泛的已发表的动力学FDG参数和GATE和XCAT平台，从10种不同的临床可接受的采样计划中进行优化采集方案。该框架还应用于6例FDG PET患者研究，证明了临床可行性。模拟和临床结果均表明，Ki图像在具有显著FDG背景浓度的肿瘤区域（如肝脏）的对比噪声比（CNRs）增强，而SUV在该区域的表现相对较差。总的来说，提出的框架能够增强对全身生理参数的量化。此外，如果真Ki对比度足够高，总采集长度可以从45min减少到~35min，并且与SUV相比，仍然可以实现改进或等效的CNR。在后续的论文中，提出了一套先进的线性回归方案，以特别解决噪声的存在，并试图在均方误差（MSE）和CNR指标之间实现更好的权衡，从而增强基于任务的成像。
Static whole body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single bed-coverage limiting the axial field-of-view to ~15–20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole body PET acquisition protocol of ~45min total length is presented, composed of (i) an initial 6-min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (6 passes x 7 bed positions, each scanned for 45sec). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares (OLS) Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of 10 different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole-body. In addition, the total acquisition length can be reduced from 45min to ~35min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error (MSE) and the CNR metrics, resulting in enhanced task-based imaging.
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影响因子：
3.1
作者：
Burger, Irene A.;Burger, Cyrill;Buck, Alfred
通讯作者：
Buck, Alfred
DOI：
10.1007/s00259-005-0063-5
发表时间：
2006-07-01
影响因子：
9.1
作者：
Dimitrakopoulou-Strauss, Antonia;Georgoulias, Vassilios;Strauss, Ludwig G.
通讯作者：
Strauss, Ludwig G.
DOI：
10.1016/j.nima.2006.10.078
发表时间：
2007-02-01
影响因子：
1.4
作者：
Gonias, P.;Bertsekas, N.;Panayiotakis, G. S.
通讯作者：
Panayiotakis, G. S.
影响因子：
--
作者：
HUANG, SC;PHELPS, ME;KUHL, DE
通讯作者：
KUHL, DE
影响因子：
8.8
作者：
Castell F;Cook GJ
通讯作者：
Cook GJ