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在临床环境中广泛使用，但仍未适用于常规临床实践的定量PET成像方法。当前使用的静态扫描可以提供全身扫描的半定量测量，标准化吸收值（SUV）。然而，它完全忽略了放射性药物动力学的动态性质。流行的半定量双重时间点方法可以通过比较活动来近似两个时间点的动力学差异，但通常需要长时间的等待时间进行第二次扫描。多个时间点方法可以计算净涌入率，但仍需要长时间的扫描持续时间，并且使整个身体扫描不可行。定量全身动态PET扫描的挑战在于如何使用短时间获得时期的数据估计定量功能值，例如净通量率，以及如何加速计算以在临床环境中使其实用。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解决这一挑战。我们利用Patlak图形分析的线性性来对每个体素的示踪剂活性建模为血液输入函数的线性组合及其积分，并由Patlak参数（包括净涌入率）加权。此外，我们基于用于从动力学区室模型得出Patlak参数的相同假设来得出一个简化的血液输入函数模型。然后，我们使用列表模式数据及其相关的不均匀泊松统计模型来估计patlak参数和血液输入函数中的参数。我们还根据噪声，分辨率和信噪比（SNR）分析了Patlak估计器的性能，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运动以实现最佳的SNR。高级估计算法以及准确的成像系统模型，可以在短的获取中使用列表模式数据稳健地计算净流入率，而无需测量的血液输入功能，并使全身动态扫描实用。我们的算法将在基于NVIDIA TESLA GPU（图形处理单元）工作站上实现，这是一种新计算工具，可提供以前仅在迷你超级计算机上可用的计算能力。我们将使用列表模式数据和系统矩阵的有效表示的组合进一步加速算法。我们将使用模拟和临床数据评估提出的方法的性能，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，包括感兴趣的区域（ROI）偏差，ROI差异，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的全身宠物成像方案，该方案可以潜在地提高肿瘤学中PET成像的敏感性和特异性。 公共卫生相关性：正电子发射断层扫描（PET）已被广泛用于癌症诊断，分期，治疗计划，管理和评估。但是，它的潜力尚未完全实现，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方法来获取和分析PET数据，这将使我们首次扫描患者的整个身体，并从动态获取的数据中对PET示踪剂的摄取进行定量估计。这些措施可能是肿瘤存在和代谢活性的更敏感的指标，因此它们的使用将导致对原发性和转移性肿瘤的检测，分期和监测。