Optimized PET Reconstruction for Cancer Detection

描述（由申请人提供）：正电子发射断层扫描（PET）中的图像重建正在达到一个成熟阶段，其中主要算法类别（例如，分析与迭代）在很大程度上被描述，主要物理过程的建模（例如衰减，散射，随机，以及最近的点扩展函数）已经建立良好，并且更复杂的问题，例如呼吸运动补偿和直接动力学参数估计正在积极追求。在研究新的PET算法和技术时，最大的挑战之一是需要有效地评估图像质量。最广泛接受的PET图像质量客观评估方法依赖于特定临床任务（或其替代品）的测量性能，这需要大量的专业知识和具有挑战性的测试数据集和方法的开发。基于我们之前在这些领域的经验，我们建议创建一个PET成像数据数据库，用于评估观察者在检测局灶性温热病变方面的表现，模拟癌症检测和分期的临床任务。该数据库将由先前R01项目下获得的现有数据集组成，并通过与两家主要PET断层扫描仪制造商的研究关系获得。该数据库将包括来自两家供应商的4张PET层析成像扫描图，包括2D、全3D和3D +飞行时间（TOF）采集模式。该数据库旨在提供一个协作共享资源，使广泛的PET研究社区能够轻松评估他们自己开发的算法和技术的病变检测性能改进。因此，该数据库将采用可移植的数据格式，并包括对研究重建软件和观察者研究工具的协作访问。这些软件工具已经在研究者的实验室里开发和使用了十年。由于数据集和观察者研究相当复杂，数据不会匿名共享；更确切地说，协作访问的目标是受助人将协同参与研究设计和执行。数据库一旦准备好并组织起来，将能够在几周的时间框架内，通过模型和人类观察者进行快速和重复的定位接收器操作特征（LROC）研究（而不是目前需要几个月到几年的时间来启动和运行此类研究）。这将在拟议研究的第二阶段得到例证，该数据库将用于解决和优化当前迭代PET重建算法中关于参数选择的几个悬而未决的问题。该项目的成功完成将为广泛客观的LROC研究提供新的研究资源，用于多个组的PET病变检测，并为最常见的临床PET应用癌症检测和分期优化几个重建参数。

DESCRIPTION (provided by applicant): Image reconstruction in positron emission tomography (PET) is reaching a maturing stage where the main algorithmic classes (e.g. analytical vs. iterative) are largely delineated, modeling of the main physics processes (e.g. attenuation, scatter, randoms, and more recently the point spread function) is well established, and more complex issues such as respiratory motion compensation and direct kinetic parameter estimation are aggressively being pursued. Once of the greatest challenges when investigating new PET algorithms and technologies is the need for efficacious assessment of image quality. The most widely accepted method for objective assessment of PET image quality relies upon measuring performance for specific clinical tasks (or surrogates thereof), which requires significant expertise and challenging development of test datasets and methodologies. Building on our previous experience in these areas, we propose to create a database of PET imaging data designed and tuned for evaluating observer performance in detecting focal warm lesions, modeling the clinical task of cancer detection and staging. This database will be comprised of existing datasets acquired under a prior R01 project and via research relationships with two major PET tomograph manufacturers. The database will include scans from four PET tomographs from two vendors operated in 2D, fully-3D, and 3D + time-of-flight (TOF) acquisition modes. The database is intended to provide a collaborative shared resource enabling the broad PET research community to easily assess lesion-detection performance improvements for their own developmental algorithms and technologies. As such, the database will be designed with portable data formats, plus include collaborative access to research reconstruction software and observer study tools. These software tools have been developed and used in the investigator's laboratory for the past decade. Since the datasets and observer studies are quite complex, data will not be shared anonymously; rather, collaborative access is targeted where the grantee will be collaboratively involved in study design and execution. The database, once prepared and organized, will enable rapid and repeat localization receiver operating characteristics (LROC) studies with both model and human observers in timeframes of several weeks (as opposed to months to years currently required for groups new to such studies to get them up and running). This will be exemplified in the second phase of the proposed research, where the database will be used to resolve and optimize several unanswered questions regarding parameter selection for current iterative PET reconstruction algorithms. Successful completion of this project will provide a new research resource enabling widespread objective LROC studies for PET lesion-detection by a number of groups, as well as optimize several reconstruction parameters for the most common clinical PET application of cancer detection and staging. PUBLIC HEALTH RELEVANCE: Positron emission tomography (PET) has experienced tremendous growth in past years, and new technologies are emerging that will push the modality even further. One of the great challenges, however, is evaluating these technological advances in terms of how the changes in image quality affect clinical tasks. This project will create a new collaborative resource for performing task-based assessment of PET image quality, and then use this resource to investigate optimal approaches for reconstructing PET images.