Characterizing, optimizing, and harmonizing cancer detection with PET imaging

项目总结 较小和早期癌症的检测和诊断显著提高了患者患癌症的几率 生死存亡。氟代脱氧葡萄糖正电子发射断层扫描(FDG-PET) 提供对正常组织和癌症组织的功能或代谢评估，自2000年以来 广泛用于临床多种癌症的检测和诊断。我们小组十多年前的研究 也有研究表明，测量和提高FDG-PET的检测能力都是可行的 通过调整采集和图像重建参数进行癌症成像。这是可以做到的 通过评估对复制人类表现的观察者模型(即放射科医生)的影响来系统地 或核医学医生)。然而，在当时，很难理解这种情况是如何在不同时期发生变化的 具有不同分辨率、敏感度和重建算法的系统，或者如果它们被操作 不同成像站点的情况有所不同。 在过去的十年中，扫描仪的分辨率、灵敏度和 重建算法，以及常规采用的飞行时间PET成像。与之平行的还有 改进了对模型观察员的理解和采用，以及采用或 跨多个PET制造商和成像站点的方法协调。最近，有一种 机器智能算法的发展，如深度神经网络，用于图像重建 和图像分析，这些都有可能提高性能。 我们建议利用这些发展来描述、优化和协调癌症 用正电子发射计算机断层扫描进行检测。三个具体目标是：(1)发展表征方法(即 FDG PET成像的检测性能的测量)。(2)使用校准到 现代物理系统，然后我们将确定如何将癌症检测作为获取功能进行优化 和图像重建参数。(3)最后，我们将开发一个平台无关(供应商无关) 可跨系统和映像站点应用的标准。这将导致多站点和 多供应商实施方法，优化癌症检测能力，从而改善患者状况 结果。

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.