Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation.
Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation.
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DOI：
10.1088/0031-9155/58/20/7419
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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;Zhou Y;Wahl RL;Rahmim A
在肿瘤学的背景下，动态PET成像与标准图形线性分析相结合，先前已被用来在体素水平上对感兴趣的示踪剂动力学参数进行定量估计，从而实现了定量PET参数成像。然而，动态PET采集协议一直局限于单一床位的有限轴向视野(~15-20 cm)，尚未被转换到全身临床成像领域。相反，标准化摄取值(SUV)PET成像被认为是临床肿瘤学的常规方法，通常涉及多个床层采集，但是静态执行的，因此不允许动态跟踪示踪剂分布。在这里，我们追求向动态全身PET参数成像的过渡，在一个统一的框架内，提出临床上可行的多床动态PET采集协议和参数成像方法。在配套的研究中，我们提出了一种新的临床可行的动态(4D)多床PET采集协议以及全身PET参数成像的概念，使用Patlak普通最小二乘(OLS)回归来估计示踪剂摄取率KI和总血液分布体积V的定量参数。在本研究中，我们提出了一种先进的混合线性回归框架，由Patlak动态体素相关性驱动，以获得比OLS为最终KI参数图像提供的对比度噪声比(CNR)和均方误差(MSE)更好的折衷，从而实现基于任务的性能优化。总体而言，无论观察者的任务是检测肿瘤还是定量评估治疗反应，所提出的统计估计框架都可以通过调整Patlak相关系数(WR)参考值来适应特定的任务表现标准。在前面的配对研究中优化的多床位动态采集方案与广泛的蒙特卡罗模拟和初始临床FDG患者数据集一起被用来验证和展示所提出的统计估计方法的潜力。模拟和临床结果都表明，在全身Patlak Ki成像的背景下，混合回归在不影响高CNR的情况下显著降低了MSE。或者，对于给定的CNR，混合回归可以比OLS更大程度地减少每张床的动态帧数量，允许更短的采集时间约30分钟，从而进一步促进拟议框架的临床采用。与SUV方法相比，全身参数成像可以提供更好的肿瘤量化，并可以作为SUV的补充，用于肿瘤检测任务。
In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15–20cm) of a single bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. 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. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical FDG patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.
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