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）开发适用于全身全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，以及（ii）确定如何通过修改临床采集方案和数据处理和图像重建程序来最大化这些测量。这项工作的动机来自于正电子发射断层扫描（PET）对与异常肿瘤代谢相关的示踪剂摄取的定量的独特灵敏度。PET肿瘤学成像的诊断效用在实践中通常受到低示踪剂摄取和低数据收集率的限制，导致图像具有高水平的统计噪声。在以前的工作中，根据这项授权，我们提出了结合使用临床可行的统计重建方法，以减少噪声传播的全三维成像的灵敏度更高。这导致了OSEM+（AW）图像重建算法的开发，该算法目前已在大多数PET扫描仪上实现。PET扫描仪设计的当前趋势是朝向具有更短患者扫描时间的3D采集模式。这强调了在3D PET成像中使用临床可行的算法来理解和控制统计噪声的首要问题。在这个竞争性的延续提案中，我们的目标是形成图像采集、处理和显示链的整体模型。该模型将用于显示如何改变3D PET采集协议、数据处理和图像重建程序可以改善与临床任务相关的特定图像质量。在实际临床约束条件下，通过优化采集协议和修改图像重建算法来降低统计噪声。我们将测试人类和数值体积观察者研究（具有三个标准正交视图）是否比传统的平面图像分析更准确地反映临床任务性能。需要这种理解统计噪声的产生、传播、减少和感知的整体方法，以允许对临床肿瘤学成像中固有的权衡进行客观选择，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.