Generation of parametric images for FDG PET using dual-time-point scans

使用双时间点扫描生成 FDG PET 参数图像

• 批准号: 9896329
• 负责人: Chi Liu
• 金额: $ 8.04万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9896329
• 关键词: Blood; Blood specimen; Cancer Patient; Clinic; Clinical; Clinical Research; Complex; Conflict (Psychology); Data; Diagnosis; Generations; Glucose; Heterogeneity; Image; Imaging Device; Inflammation; Injections; Label; Least-Squares Analysis; Malignant Neoplasms; Methods; Modeling; Noise; Normal tissue morphology; Oncology; Patients; Plasma; Positron-Emission Tomography; Protocols documentation; Radiation exposure; Radiolabeled; Scanning; Standardization; Techniques; Time; Tracer; Training; Translations; Variant; X-Ray Computed Tomography; attenuation; base; clinical investigation; clinical practice; cohort; convolutional neural network; cost; deep learning; diagnostic accuracy; image reconstruction; improved; indexing; individual patient; innovation; novel; parametric imaging; population based; research study; simulation; success; tumor; uptake

Project Summary/Abstract Positron emission tomography combined with computed tomography (PET/CT) using the radiolabeled tracer 2- deoxy-2-(18F)fluoro-D-glucose (FDG) has become a standard imaging tool for cancer patient management. The semi-quantitative parameter standardized uptake value (SUV) is routinely used in clinical for tumor uptake quantification, which is computed on the static PET image acquired at a certain time (typically 60 min) post tracer injection for a short interval (typically 5-15 min). However, the quantification accuracy of SUV from a single PET scan suffers from the variabilities of tracer plasma clearance and acquisition start time. The dual- time-point FDG PET imaging has been intensively investigated and used in both clinical and research studies, typically one scan at 60 min and the other at 120 min, showing the potential to enhance the diagnostic accuracy of FDG PET by differentiating malignancy from inflammation and normal tissue. However, the current clinical dual-time-point FDG PET studies use the relative SUV change between two scans as the quantification index, which cannot eliminate the variations in tracer plasma clearance. Meanwhile, the dual-time-point protocol has not been optimized and standardized currently, leading to conflicting results. The fully-quantitative parameter, tracer net uptake rate constant Ki, is the most accurate parameter to quantify FDG PET, which is calculated using dynamic imaging with compartmental modeling. Ki is independent on the plasma clearance or acquisition start time. However, the long and complex acquisition protocol (typically at least 60 min), which requires dynamic scanning and sequential arterial blood sampling (or image-derived blood activity) used as input function from the time of injection, limits its application in clinical practice. Meanwhile, generation of the parametric Ki image, which can provide additional heterogeneity information for FDG PET, is challenging clinically using voxel-by-voxel compartmental modeling due to the computational cost and being sensitive to noise using non-linear least squares. The graphical Patlak plot, can be used for simplified Ki calculation and Ki image generation by voxel-by-voxel fitting. However, it still needs dynamic scanning starting from 15-30 min after injection and input function from the time of injection. The aims of this proposal are 1) to optimize the dual-time-point protocol for accurate Ki quantification using Patlak plot without the need for individual patient's input function, and 2) to generate high-quality low-noise dual-time-point Ki images using novel techniques based on deep learning. Upon the success of this project, our proposed approach can obtain reliable tumor Ki quantification and parametric Ki image "for free" without adding any additional complexity on the existing dual- time-point protocol currently used in clinical practice, with great potential of improving diagnosis and therapy assessment in oncology. We expect the translation of this approach to clinical investigation to be fast, as this is a post-processing approach and is based on data already acquired using clinically used protocol without imposing additional burden to technologists.

项目摘要/摘要 使用放射性标记示踪剂的正电子发射断层扫描和计算机断层扫描(PET/CT) 脱氧-2-(18F)氟-D-葡萄糖(FDG)已成为癌症患者治疗的标准成像工具。 半定量参数标准化摄取值(SUV)是临床上常用的肿瘤摄取参数 量化，对在特定时间(通常为60分钟)后采集的静态PET图像进行计算 示踪剂注入时间间隔短(通常为5-15分钟)。然而，从一辆SUV的量化精度来看， 单次正电子发射计算机断层扫描受示踪血浆清除率和采集开始时间变化的影响。双重的-- 时间点FDG PET成像已被广泛研究并在临床和研究中使用， 通常，一次扫描在60分钟，另一次在120分钟，显示了提高诊断的潜力 FDGPET鉴别恶性肿瘤、炎症和正常组织的准确性。然而，目前 临床双时间点FDG PET研究使用两次扫描之间的相对SUV变化作为量化 指数，这不能消除示踪等离子体清除率的变化。同时，双时间点 协议目前还没有得到优化和规范，导致结果相互冲突。完全量化的 示踪剂净摄取速率常数KI是定量FDG PET最准确的参数 使用动态成像和隔室建模进行计算。KI独立于血浆清除量或 采集开始时间。然而，漫长而复杂的捕获协议(通常至少60分钟)， 需要动态扫描和顺序动脉血液采样(或图像衍生的血液活动)，用作 输入函数从注射时开始，限制了其在临床实践中的应用。同时，新一代的 参数KI图像可以为FDG PET提供额外的异质性信息，这是具有挑战性的 临床上使用逐体素的隔室模型由于计算成本高且对 使用非线性最小二乘的噪声。图形化的Patlak图，可用于简化KI计算和KI 通过逐个体素拟合生成图像。但是，从15-30分钟开始，仍然需要动态扫描 注射后和输入功能从注射时开始。这项建议的目的是1)优化 利用Patlak图精确定量KI的双时间点方案，无需患者个体 输入功能，以及2)使用新技术生成高质量、低噪声的双时间点KI图像 基于深度学习。在本项目成功后，我们提出的方法可以获得可靠的肿瘤KI 在不增加现有DUAL-DUAL的任何额外复杂性的情况下，将量化和参数KI图像“免费”- 目前在临床实践中使用的时间点方案，具有极大的提高诊断和治疗的潜力 肿瘤学的评估。我们预计这种方法将很快转化为临床研究，因为这是 一种后处理方法，基于已使用临床使用的协议获取的数据，而无需 给技术人员带来额外的负担。