Quantitative Methods for Clinical Whole Body Dynamic PET

描述（由申请人提供）：正电子发射断层扫描（PET）是肿瘤学中的主要分子成像工具，应用范围从诊断和分期到患者管理。尽管PET在临床环境中广泛使用，但常规临床实践中没有可用的定量PET成像方法。目前使用的静态扫描可以为全身扫描提供半定量测量，标准化摄取值（SUV）。然而，它完全忽略了放射性药物动力学的动态性质。流行的半定量双时间点方法可以通过比较活性来近似两个时间点的动力学差异，但通常需要延长第二次扫描的等待时间。多时间点方法可以计算净流入率，但仍然需要长的扫描持续时间，使全身扫描不可行。定量全身动态PET扫描的挑战在于如何使用来自短采集周期的数据来估计定量功能值（例如净通量率），以及如何加速计算以使其在临床环境中实用。我们通过开发和优化一种新的数据分析方法并使用高性能计算工具实现它来应对这一挑战。我们利用Patlak图形分析的线性，将每个体素中的示踪剂活性建模为血液输入函数及其积分的线性组合，由包括净流入率的Patlak参数加权。此外，我们基于与从动力学室模型推导Patlak参数相同的假设，推导出血液输入函数的简化模型。然后，我们估计Patlak参数和参数的血液输入函数中的惩罚最大似然估计框架使用列表模式数据及其相关的非齐次泊松统计模型。我们还从理论上分析了我们的Patlak估计器在噪声，分辨率和信噪比（SNR）方面的性能，并使用结果来指导我们优化扫描持续时间和成像床的任何移动，以实现最佳SNR。先进的估计算法，沿着一个精确的成像系统模型，可以在没有测量的血液输入函数的情况下，在短采集中使用列表模式数据稳健地计算净流入率，并使全身动态扫描变得实用。我们的算法将在基于Nvidia Tesla GPU（图形处理单元）的工作站上实现，这是一种新的计算工具，可提供以前仅在迷你超级计算机上提供的计算能力。我们将使用列表模式数据和系统矩阵的有效表示的组合来进一步加速我们的算法。我们将评估所提出的方法的性能，并将其与SUV，双时间点方法和传统的Patlak方法进行比较，使用模拟和临床数据。我们将使用一系列性能指标，包括感兴趣区域（ROI）偏差、ROI方差、病变可检测性以及计算机和人类观察员。该项目最终将提供一种定量动态全身PET成像方案，可能会提高PET成像在肿瘤学中的灵敏度和特异性。

DESCRIPTION (provided by applicant): Positron Emission tomography (PET) is a major molecular imaging tool in oncology, with applications ranging from diagnosis and staging to patient management. Despite the broad use of PET in the clinical environment, there is no quantitative PET imaging method available for routine clinical practice. The currently used static scan can provide a semi-quantitative measurement, standardized uptake value (SUV), for a whole body scan. However, it completely ignores the dynamic nature of radiopharmaceutical kinetics. The popular semi-quantitative dual time point method can approximate the kinetic differences at two time points by comparing activities but usually requires an extended waiting time for the second scan. The multiple time point method can calculate the net influx rate but still requires long scan duration and makes a whole body scan infeasible. The challenge of a quantitative whole body dynamic PET scan lies in how to estimate the quantitative functional values, such as net flux rate, using data from a short acquisition period, and how to accelerate the computation to make it practical in a clinical setting. We address this challenge by developing and optimizing a novel data analysis method and implementing it using a high performance computing tool. We take advantage of the linearity of Patlak graphic analysis to model the tracer activity in each voxel as a linear combination of the blood input function and its integral, weighted by the Patlak parameters including net influx rate. In addition, we derive a simplified model of the blood input function, based on the same assumptions used to derive Patlak parameters from the kinetic compartment model. We then estimate the Patlak parameters and the parameters in the blood input function in a penalized maximum likelihood estimation framework using the list mode data and its associated inhomogeneous Poisson statistical model. We also theoretically analyze the performance of our Patlak estimator in terms of noise, resolution and signal-to-noise ratio (SNR), and use the results to guide us in optimizing the scan duration and any movement of imaging bed to achieve the best SNR. The advanced estimation algorithm, along with an accurate imaging system model, can robustly compute the net influx rate using the list mode data in a short acquisition without a measured blood input function, and make whole body dynamic scans practical. Our algorithms will be implemented on an Nvidia Tesla GPU (graphics processing unit) based workstation, a new computing tool that provides computational power previously available only on a mini super computer. We will further accelerate our algorithms using a combination of efficient representation of the list mode data and the system matrix. We will evaluate the performance of the proposed method and compare it with SUV, the dual time point method, and the traditional Patlak method, using simulated and clinical data. We will use a range of performance metrics, including region of interest (ROI) bias, ROI variance, lesion detectability, and computer and human observers. This project will eventually provide a quantitative dynamic whole body PET imaging protocol that can potentially improve the sensitivity and specificity of PET imaging in oncology.