STATISTICAL METHODS FOR QUANTITATIVE PET

描述(改编自申请人摘要)：新一代PET 扫描仪通过使用正在接近医疗设备的分辨率极限 新的闪烁体、更小的探测器和3D数据采集。然而， 最常用的滤波反投影(FBP)图像重建方法 临床环境无法充分实现这些系统的潜力 因为它们没有考虑到数据的光子限制性质或 重合数据不能代表完全平行的事实 单独的探测器对。本课题的研究主题是PET图像 重建最好在统计框架内进行，在该框架中 观测数据的物理和统计属性是准确的 模特儿。在当前资助期内，FAST 2D和3D贝叶斯 重建方法被开发出来，将准确的统计和 符合探测的物理模型和图像模型设计成 反映体内示踪剂分布的分段平滑性质。这些 这些方法已经与标准的临床方案进行了比较，并表明 在匹配的背景噪声水平下提高分辨率， 定量准确性、对比度恢复和使用 观察者。在拟议的项目期内，它们将继续延长 针对一系列最先进扫描仪的贝叶斯重建方法： (I)全身CTI Exact HR+，(Ii)新型CTI高分辨率脑部扫描仪 使用基于LSO的相互作用深度探测器，(Iii)同步X射线 CTI和加州大学联合开发的CT/PET扫描仪 匹兹堡，(Iv)加州大学洛杉矶分校的microPET小动物扫描仪，和(V) 加州大学洛杉矶分校正在开发基于LSO的专用PET乳房X光摄影系统。他们 将使用因式分解矩阵方法来模拟正电子射程和光子对 晶体的角度分离、几何和本征探测器效率 穿透和分散，以及停滞时间。在共轭梯度内 贝叶斯图像重建框架他们将包括一系列吉布斯 先验(例如，凸对平滑，基于解剖的先验， 统一分辨率的空间变化加权)和似然 函数(例如，泊松、高斯近似、修改的泊松模型 考虑随机减法的影响)。它们将包括已订购的 代码中EM(OSEM)实施选项的子集，以进行比较 目的，并允许快速重建。他们还将实施一份清单 我们的方法的模似然变化。优化的多线程 这些图像重建方法的实现将被开发用于 用于单处理器和多处理器的UNIX工作站以及Pentium-Pro服务器。 贝叶斯、OSEM和优化的FBP重建方法的性能将 通过分辨率研究、信噪比测量、 定量和病变检测。关于小型和小型的定量研究 形状不规则的感兴趣区域，包括考虑部分 音量效果将使用幻影和组合 小动物的放射自显影、磁共振和正电子发射体层摄影研究。病变检测将 使用真实的乳房和胸部模型的数据进行研究，并进行模拟 损伤被添加到人类数据中。我们的优化软件将可用 我们的合作者和其他感兴趣的研究网站。

DESCRIPTION (Adapted from Applicant's Abstract): The new generation of PET scanners is approaching the resolution limit of the modality through the use of new scintillators, smaller detectors and 3D data acquisition. However the filtered backprojection (FBP) image reconstruction schemes used in most clinical settings are unable to fully realize the potential of these systems as they do not take account of the photon-limited nature of the data or the fact that the coincidence data do not represent perfect collimation between individual detector pairs. The thesis of this project is that PET image reconstruction is best performed within a statistical framework in which the physical and statistical properties of the observed data are accurately modeled. In the current funding period fast 2D and 3D Bayesian reconstruction methods were developed which combine accurate statistical and physical models of coincidence detection with image models designed to reflect the piece-wise smooth nature of in-vivo tracer distributions. These methods have been compared to standard clinical protocols and shown to produce improvements in resolution at matched background noise levels, quantitative accuracy, contrast recovery, and lesion detection using observers. During the proposed project period they will continue to extend the Bayesian reconstruction methods to a range of state of the art scanners: (I) whole body CTI EXACT HR+, (ii) the new CTI high resolution brain scanner with LSO based depth-of-interaction detectors, (iii) the simultaneous x-ray CT/PET scanner being developed jointly by CTI and the University of Pittsburgh, (iv) the UCLA microPET small animal scanner, and (v) the dedicated LSO-based PET mammography system under development at UCLA. They will use the factored matrix method to model positron range and photon-pair angular separation, geometric and intrinsic detector efficiencies, crystal penetration and scatter, and dead-time. Within the conjugate gradient Bayesian image reconstruction framework they will include a range of Gibbs priors (e.g. convex pair-wise smoothing, anatomically-based priors, spatially variant weightings for uniform resolution) and likelihood functions (e.g. Poisson, Gaussian approximations, modified Poisson models to account for the effects of randoms subtraction). They will include Ordered Subsets EM (OSEM) implementation options in the code for comparative purposes and to allow fast reconstruction. They will also implement a list mode likelihood variation of our methods. Optimized multithreaded implementations of these image reconstruction methods will be developed for use on single and multiprocessor UNIX workstations and Pentium-Pro servers. Performance of Bayesian, OSEM and optimized FBP reconstruction methods will be evaluated through studies of resolution, signal to noise measures, quantitation and lesion detection. Quantitation studies over small and irregularly shaped regions of interest that include consideration of partial volume effects will be performed using phantoms and combined autoradiography, MR and PET studies of small animals. Lesion detection will be studied using data from a realistic breast & thorax phantom and simulated lesions added to human data. Our optimized software will be made available to our collaborators and other interested research sites.