Optimization of PET Imaging

PET 成像的优化

• 批准号: 6611945
• 负责人: JINYI QI
• 金额: $ 27.9万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2007-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6611945
• 关键词: bioimaging /biomedical imaging; clinical research; computer assisted diagnosis; human data; image enhancement; mathematical model; phantom model; positron emission tomography; statistics /biometry; whole body imaging /scanning

DESCRIPTION (provided by applicant): Iterative reconstruction algorithms that significantly improve image quality over filtered backprojection methods have been developed by us and others. However, the full potential of these algorithms have not yet been realized. For example, iterative reconstruction methods can incorporate sophisticated modeling of the expected image that is specific for classes of clinical applications, and use of iterative methods can substantially remove constraints on PET instrument designs (e.g., allow the use of non-standard detector geometries). The goal of this grant is to develop a theoretical framework that can be used in practice to obtain the optimum iterative reconstruction algorithm as well as the optimum PET instrument design for each clinical application and to validate the new approaches in order to attain the full potential of PET. The potential application of this work is enabled by the availability of high performance computers that allow the use of accurate and statistically sophisticated iterative reconstruction algorithms. We propose to incorporate stochastic models for the target and background as a major extension of our previous work. The new approach to the optimization of the reconstruction algorithm will use the initial noisy data set to estimate the resolution and the noise characteristics of the target and the background at each element of the image. Numerical observers will be used to analytically compute task-specific figures of merit. A spatially variant image prior model will then be designed to achieve the optimal lesion detection and quantitation across the whole region of interest. Finally, the theoretical results of the optimum reconstruction, combined with ensemble information of patients and of the disease pertaining to a specific organ, will be used to discover the optimal instrument design for each clinical task. We will study both whole-body PET systems and application specific PET systems with non-standard geometries (e.g., PET for imaging breast and prostate). All results will be validated using Monte Carlo simulations, phantom scans, and real patient data from a whole body scanner.

描述（由申请人提供）： 我们和其他人已经开发了迭代重建算法，这些算法显着提高了过滤的反向投影方法。但是，这些算法的全部潜力尚未实现。例如，迭代重建方法可以结合针对临床应用类别的预期图像的复杂建模，并且使用迭代方法可以实质上消除对PET仪器设计的限制（例如，允许使用非标准探测器几何形状）。该赠款的目的是开发一个理论框架，可以在实践中使用该框架，以获得最佳的迭代重建算法以及每个临床应用的最佳PET仪器设计，并验证新方法以实现PET的全部潜力。高性能计算机的可用性可以实现这项工作的潜在应用，这些计算机允许使用准确且统计上复杂的迭代重建算法。我们建议将目标和背景的随机模型纳入我们先前工作的主要扩展。重建算法优化的新方法将使用初始噪声数据集来估计图像每个元素处的目标和噪声特征和背景。数值观察者将用于分析计算特定于任务的功绩数字。然后，将设计一个空间变体图像先验模型，以实现整个关注区域的最佳病变检测和定量。最后，最佳重建的理论结果，结合了患者的整体信息以及与特定器官有关的疾病的整体信息，将用于发现针对每个临床任务的最佳仪器设计。我们将研究具有非标准几何形状的全身宠物系统和应用特定的宠物系统（例如，用于成像乳房和前列腺的PET）。所有结果将通过蒙特卡洛模拟，幻影扫描和全身扫描仪的真实患者数据进行验证。