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.

项目摘要 较小和更早的癌症的检测和诊断可显着提高患者的机会 生存。使用氟18-氟脱氧葡萄糖（FDG-PET）的正电子发射断层扫描（PET）成像 提供正常组织和取消组织的功能或代谢评估，自2000年以来 在临床上广泛用于检测和诊断许多癌症。十年前我们小组的研究 其他人则表明，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对癌症进行成像。可以做到 通过评估复制人类绩效的观察者模型的影响（即放射学家 或核医学医师）。然而，当时，了解这种情况如何变化是一个挑战 具有不同分辨率，灵敏度和重建算法的系统，或者是否操作 在成像站点之间的不同。 在过去的十年中，扫描仪的分辨率，灵敏度和 重建算法以及飞行时间宠物成像的常规采用。并行 是对模型观察者的理解和采用，以及采用途径或 跨多个宠物制造商和成像站点的方法协调。最近有 这两个图像重建 和图像分析，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 用宠物成像检测。三个具体目的是：（1）开发表征的方法（即 测量FDG PET成像的检测性能。 （2）使用校准到A的模型系统 然后，我们将确定如何优化癌症检测作为获取的函数 和图像重建参数。 （3）最后，我们将开发独立于平台的（供应商不可知论） 可以跨系统和成像站点应用的标准。这将导致多站点的路线图和 多供应商实施方法优化癌症可检测性并因此改善了患者 结果。