Quantitative Methods for Clinical Whole Body Dynamic PET

临床全身动态 PET 的定量方法

• 批准号: 8237421
• 负责人: Quanzheng Li
• 金额: $ 38.19万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-12-15 至 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8237421
• 关键词: Accounting; Address; Algorithms; Beds; Biological Models; Blood; Blood specimen; Calibration; Characteristics; Clinical; Clinical Data; Colorectal Cancer; Computers; Data; Data Analyses; Detection; Diagnosis; Environment; Evaluation; Evaluation Studies; Functional Imaging; Goals; High Performance Computing; Human; Image; Imaging Device; Information Systems; Kinetics; Lead; Lesion; Measurement; Measures; Metabolic; Metastatic Neoplasm to the Liver; Methods; Metric; Modeling; Monitor; Movement; Nature; Neoplasm Metastasis; Noise; Output; Patients; Pattern; Performance; Physicians; Positioning Attribute; Positron-Emission Tomography; Primary Neoplasm; Procedures; Process; Property; Protocols documentation; Radiopharmaceuticals; Resolution; Running; Scanning; Sensitivity and Specificity; Signal Transduction; Simulate; Staging; Statistical Models; System; Time; Tracer; Weight; Work; attenuation; base; cancer diagnosis; clinical application; clinical practice; computerized tools; cost; disease diagnosis; human data; imaging modality; improved; interest; molecular imaging; multithreading; novel; novel strategies; oncology; patient population; physical model; research clinical testing; tool; treatment planning; tumor; uptake; whole body imaging

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. PUBLIC HEALTH RELEVANCE: Positron Emission Tomography (PET) has been widely used in cancer diagnosis, staging, treatment planning, management and evaluation. However, its potential is not yet fully realized, in part because we are not able to take full advantage of the dynamic information that can be collected by the PET scanner. In this project we will develop a new approach to the acquisition and analysis of PET data that will allow us for the first time to scan the whole body of the patient and produce quantitative estimates of PET tracer uptake from dynamically acquired data. These measures may be more sensitive indicators of the presence and metabolic activity of tumors, so that their use would lead to improved detection, staging and monitoring of primary and metastatic tumors.

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，其应用范围从诊断和分期到患者管理。尽管 PET 在临床环境中得到广泛应用，但尚无定量 PET 成像方法可用于常规临床实践。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，但仍然需要较长的扫描持续时间，并且使得全身扫描不可行。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颖的数据分析方法并使用高性能计算工具来实施该方法来应对这一挑战。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，并由包括净流入率在内的 Patlak 参数进行加权。此外，我们基于用于从动力学室模型导出 Patlak 参数的相同假设，导出了血液输入函数的简化模型。然后，我们使用列表模式数据及其相关的非齐次泊松统计模型在惩罚最大似然估计框架中估计 Patlak 参数和血液输入函数中的参数。我们还从理论上分析了 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 。先进的估计算法与精确的成像系统模型一起，可以在没有测量血液输入功能的情况下，在短时间内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，可提供以前只能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的有效表示和系统矩阵的组合来进一步加速我们的算法。我们将使用模拟和临床数据评估所提出方法的性能，并将其与 SUV、双时间点方法和传统 Patlak 方法进行比较。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者。该项目最终将提供定量动态全身 PET 成像方案，有望提高肿瘤学 PET 成像的敏感性和特异性。 公共健康相关性：正电子发射断层扫描 (PET) 已广泛应用于癌症诊断、分期、治疗计划、管理和评估。然而，其潜力尚未完全实现，部分原因是我们无法充分利用 PET 扫描仪可以收集的动态信息。在这个项目中，我们将开发一种新的 PET 数据采集和分析方法，这将使我们能够首次扫描患者的全身，并从动态采集的数据中生成 PET 示踪剂吸收的定量估计。这些措施可能是肿瘤存在和代谢活动的更敏感指标，因此它们的使用将改善原发性和转移性肿瘤的检测、分期和监测。