Strategies for Clinical Oncology Imaging with 3D PET

描述(由申请人提供)：本项目的总体目标是：(I)开发适用于全身三维正电子发射断层扫描(PET)肿瘤成像的图像质量定量测量方法，以及(Ii)确定如何通过修改用于数据处理和图像重建的临床采集协议和程序来最大化这些测量方法。这项工作的动机来自于正电子发射断层扫描(PET)在定量与肿瘤代谢异常相关的示踪剂摄取方面的独特敏感性。在实践中，由于示踪剂摄取率低和数据采集率低，PET肿瘤学成像的诊断用途往往受到限制，导致图像具有高水平的统计噪声。在之前的工作中，根据这项拨款，我们建议将全3D成像的更高灵敏度与临床上可行的统计重建方法相结合，以减少噪声传播。这导致了FORE+(AW)OSEM图像重建算法的发展，该算法现在已在大多数PET扫描仪上实现。目前PET扫描仪设计的趋势是采用3D采集模式，患者扫描时间更短。这强调了在3DPET成像中使用临床上可行的算法来理解和控制统计噪声的首要问题。在这个相互竞争的延续方案中，我们的目标是形成一个图像采集、处理和显示链的整体模型。该模型将用于展示改变3DPET采集协议、数据处理和图像重建程序如何改善与临床任务相关的特定图像质量。在实际的临床约束下，将通过优化采集协议和修改图像重建算法来减少统计噪声。我们将测试人类和数字体积观察者研究(使用三个标准的正交视图)是否比传统的平面图像分析更准确地反映临床任务表现。这种全面了解统计噪声的产生、传播、减少和感知的方法是必要的，以允许对临床肿瘤学成像中固有的权衡进行客观选择，以实现完全3D-PET全身成像的全部潜力，并最大限度地提高其对患者管理的影响。

DESCRIPTION (provided by applicant): The overall goals of this project are to: (i) develop quantitative measures of image quality appropriate for whole body fully-3D positron emission tomography (PET) oncology imaging, and (ii) to determine how to maximize these measures by modifying clinical acquisition protocols and procedures for data processing and image reconstruction. The motivation for this work arises from the unique sensitivity of positron emission tomography (PET) for quantitation of tracer uptake associated with abnormal tumor metabolism. The diagnostic utility of PET oncology imaging is often limited in practice by low tracer uptake and low data collection rates, resulting in images with high levels of statistical noise. In previous work under this grant we proposed combining the higher sensitivity of fully-3D imaging with the use of clinically feasible statistical reconstruction methods to reduce noise propagation. This led to the development of the FORE+(AW)OSEM image reconstruction algorithm, which is now implemented on most PET scanners. Current trends in PET scanner design are towards 3D acquisition modes with even shorter patient scan times. This emphasizes the paramount problem of understanding and controlling statistical noise with clinically feasible algorithms in 3D PET imaging. In this competing continuation proposal, our goal is to form an overall model for the chain of image acquisition, processing, and display. That model will be used to show how changing the 3D PET acquisition protocols, data processing, and image reconstruction procedures can improve specific image qualities relevant to clinical tasks. Reducing statistical noise will be addressed both by optimizing the acquisition protocol and modifying the image reconstruction algorithms, within practical clinical constraints. We will test whether both human and numerical volumetric observer studies (with the three standard orthogonal views) reflect clinical task performance more accurately than traditional planar image analyses. This overall approach of understanding the generation, propagation, reduction, and perception of statistical noise is needed to allow objective choices about the tradeoffs inherent in clinical oncology imaging, to realize the full potential of fully 3D-PET whole body imaging and maximize its impact on patient management.