Optimized PET Reconstruction for Cancer Detection

描述（申请人提供）：正电子发射断层扫描（PET）中的图像重建正达到成熟阶段，其中主要算法类（例如，分析与迭代）在很大程度上被描绘，主要物理过程的建模（例如衰减、散射、随机数，以及最近的点扩散函数）已经很好地建立，并且更复杂的问题如呼吸运动补偿和直接动力学参数估计正在积极地进行。在研究新的PET算法和技术时，最大的挑战之一是需要有效评估图像质量。PET图像质量客观评估的最广泛接受的方法依赖于测量特定临床任务（或其替代品）的性能，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。基于我们以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，该数据库设计并调整用于评估观察者在检测局灶性温热病变、模拟癌症检测和分期的临床任务方面的表现。该数据库将由先前R 01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四台PET断层扫描仪的扫描，这些断层扫描仪以2D、全3D和3D +飞行时间（TOF）采集模式运行。该数据库旨在提供协作共享资源，使广泛的PET研究社区能够轻松评估其自身开发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员研究工具的协作访问。这些软件工具是过去十年来在调查员实验室开发和使用的。由于数据集和观察员研究相当复杂，数据将不会匿名共享;相反，协作访问的目标是受让人将协作参与研究设计和执行。该数据库一旦准备和组织，将使快速和重复的定位接收器操作特性（LROC）研究与模型和人类观察员在几周的时间范围内（而不是几个月到几年，目前需要新的小组这样的研究，让他们启动和运行）。这将在拟议研究的第二阶段中举例说明，其中数据库将用于解决和优化当前迭代PET重建算法的参数选择相关的几个未回答的问题。该项目的成功完成将提供一个新的研究资源，使广泛的客观LROC研究的PET病变检测的一些群体，以及优化几个重建参数的最常见的临床PET应用的癌症检测和分期。

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.