Optimization of PET Imaging

描述（由申请人提供）： 迭代重建算法，显着提高图像质量的滤波反投影方法已经开发了我们和其他人。然而，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特定于临床应用类别的预期图像的复杂建模，并且迭代方法的使用可以基本上去除对PET仪器设计的约束（例如，允许使用非标准检测器几何形状）。该资助的目标是开发一个理论框架，可用于实践，以获得最佳的迭代重建算法以及最佳的PET仪器设计，为每个临床应用，并验证新的方法，以实现PET的全部潜力。这项工作的潜在应用是由高性能计算机的可用性，允许使用准确的和统计上复杂的迭代重建算法。我们建议将随机模型的目标和背景作为我们以前的工作的一个主要扩展。新的重建算法的优化方法将使用初始噪声数据集来估计图像的每个元素处的目标和背景的分辨率和噪声特性。数值观测器将用于分析计算特定任务的品质因数。然后将设计空间变化图像先验模型以实现整个感兴趣区域的最佳病变检测和定量。最后，最佳重建的理论结果，结合患者的整体信息和与特定器官有关的疾病，将用于发现每个临床任务的最佳仪器设计。我们将研究全身PET系统和具有非标准几何形状的特定应用PET系统（例如，PET用于乳房和前列腺成像）。将使用蒙特卡罗模拟、体模扫描和来自全身扫描仪的真实的患者数据对所有结果进行确认。

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.