Characterizing, optimizing, and harmonizing cancer detection with PET imaging

通过 PET 成像表征、优化和协调癌症检测

• 批准号: 10579947
• 负责人: Paul E. Kinahan
• 金额: $ 66.68万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-25 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579947
• 关键词: Adoption; Algorithms; Artificial Intelligence; Biological Models; Calibration; Cancer Detection; Cancerous; Cause of Death; Clinical; Data; Detection; Development; Diagnosis; Diagnostic Neoplasm Staging; Discipline of Nuclear Medicine; Disease; Fluorine; Goals; Human; Hybrids; Image; Image Analysis; Imaging technology; Infiltration; Institution; Lesion; Link; Malignant Neoplasms; Manuals; Manufacturer; Measurement; Measures; Medical; Metabolic; Methods; Modeling; Modernization; Morbidity - disease rate; Outcome; Pathway interactions; Patient-Focused Outcomes; Patients; Performance; Physicians; Positron-Emission Tomography; Protocols documentation; Public Health; Publishing; Recurrent Malignant Neoplasm; Resolution; Screening for cancer; Site; Staging; System; Time; Tissues; Translations; United States; Variant; Vendor; X-Ray Computed Tomography; X-Ray Computed Tomography Scanners; artificial intelligence method; cancer diagnosis; cancer imaging; cancer recurrence; deep learning model; deep neural network; detection limit; detection method; fluorodeoxyglucose; image reconstruction; imaging system; improved; industry partner; intelligent algorithm; loss of function; lymph nodes; novel; open data; platform-independent; radiologist; reconstruction; simulation; tomography; tool; tumor

Project summary Detection and diagnosis of smaller and earlier-stage cancers significantly improves a patient's chances of survival. Positron emission tomography (PET) imaging using fluorine 18–fluorodeoxyglucose (FDG-PET) provides a functional or metabolic assessment of normal versus cancerous tissues, and since 2000 has been widely used clinically for the detection and diagnosis of many cancers. Studies over a decade ago by our group and others had shown that it was feasible to both measure and improve the detection ability of FDG-PET imaging for cancer by adjusting acquisition and image reconstruction parameters. This could be done systematically by evaluating the effect on observer models that replicated human performance (i.e. radiologists or nuclear medicine physicians). At the time, however, it is challenging to understand how this varied across systems with different resolutions, sensitivities, and reconstruction algorithms, or if they were operated differently across imaging sites. Over the last decade there have been dramatic improvements in scanner resolution, sensitivity, and reconstruction algorithms, as well as the routine adoption of time-of-flight PET imaging. In parallel there has been an improved understanding and adoption of model observers, as well as pathways for adoption or harmonization of methods across multiple PET manufacturers and imaging sites. Most recently there has been the development of machine intelligence algorithms, such deep neural networks, for both image reconstruction and image analysis, which have the potential to improve performance. We are proposing to take advantage of these developments to characterize, optimize, and harmonize cancer detection with PET imaging. The three specific aims are: (1) Develop methods for characterization (i.e. measurement) of detection performance for FDG PET imaging. (2) Using a model system calibrated to a modern physical system we will then determine how to optimize cancer detection as a function of acquisition and image reconstruction parameters. (3) Finally we will develop a platform-independent (vendor agnostic) standard that can be applied across systems and imaging sites. This will lead to a roadmap for multi-site and multi-vendor implementation approaches that optimizing cancer detectability and thus improved patient outcomes.

项目概要 较小和早期癌症的检测和诊断显着提高了患者的机会 使用氟 18-氟脱氧葡萄糖 (FDG-PET) 进行正电子发射断层扫描 (PET) 成像。 提供正常组织与癌组织的功能或代谢评估，自 2000 年以来一直在 我们小组十多年前的研究在临床上广泛用于许多癌症的检测和诊断。 等人表明测量和提高FDG-PET的检测能力是可行的 通过调整采集和图像重建参数来进行癌症成像。 通过评估对复制人类表现的观察者模型（即放射科医生 然而，当时很难理解这种情况在不同时期有何不同。 具有不同分辨率、灵敏度和重建算法的系统，或者它们是否被操作 不同成像部位的情况不同。 在过去的十年中，扫描仪分辨率、灵敏度和 重建算法，以及飞行时间 PET 成像的常规采用。 提高了对模型观察员的理解和采用，以及采用或采用的途径 最近，多个 PET 制造商和成像站点之间的方法得到了统一。 用于图像重建的机器智能算法（例如深度神经网络）的开发 和图像分析，这有可能提高性能。 我们建议利用这些发展来表征、优化和协调癌症 PET 成像检测的三个具体目标是：（1）开发表征方法（即 (2) 使用校准到的模型系统 现代物理系统然后我们将确定如何根据采集的函数来优化癌症检测 (3) 最后我们将开发一个独立于平台的（与供应商无关的）。 可以跨系统和成像站点应用的标准，这将导致多站点和成像站点的路线图。 优化癌症检测能力从而改善患者的多供应商方法 结果。