RECONSTRUCTION AND ANALYSIS OF EMISSION TOMOGRAPHY DATA

发射计算机体层摄影术(ECT)是一种重要的检测手段 损伤和体内生理功能的研究。因此，它 在肿瘤的定位和诊断中起着至关重要的作用 他们的转移状态。它还有助于发展新的 需要对代谢过程进行量化的医疗技术 示例性单抗方案。这个项目的目标是 最大限度地提高从ECT数据中提取的诊断信息量 执行这些临床任务。 为实现这一目标将采用的机制是 产生ECT数据的物理过程的统计模型和 它们的关联源分布。中开发的统计模型 该项目包含了与肿瘤直接相关的参数 检测和定位。从观测数据估计这些参数 ECT数据允许量化平均病变活动水平、病变 卷，并提供了一个。ECT图像的自动分割为 活动分化区。它还允许临床医生在 ECT图像的重建，从而提高了结果的精度 通过结合临床知识进行重建 解剖结构。最后，所提出的模型允许信息 从相互关联的(空间匹配的)结构图像(例如 磁共振或计算机断层扫描图像)合并到 功能性ECT图像的重建。重要的是，这个结构 信息不需要精确地指定：统计模型允许 根据观测数据移动预定边界 似然函数。 ECT图像临床评价的一个重要方面是程度 确定图像特征的确定性。这个 在该项目中开发的统计模型允许直接评估 这种形象的不确定性。因此，统计检验是否存在 肿瘤活动和肿瘤活动的可信区间是可用的 可以构建体积。

Emission computed tomography (ECT) is an important tool in the detection of lesions and in the study of in vivo physiological functions. It therefore plays a critical role in the localization of tumors and determination of their metastatic status. It also contributes to the development of new medical technologies requiring quantification of metabolic processes, for example monoclonal antibody protocols. The objective of this project is to maximize the amount of diagnostic information extracted from ECT data to perform these clinical tasks. The mechanism that will be employed to accomplish this objective is a statistical model for the physical processes that generate ECT data and their associated source distributions. The statistical model developed in this project incorporates parameters that directly relate to tumor detection and localization. Estimation of these parameters from observed ECT data permits quantification of mean lesion activity levels, lesion volumes, and provides for an. automatic segmentation of ECT images into activity differentiated regions. It also permits clinician interaction in the reconstruction of ECT images, thus increasing the accuracy of resultant reconstructions through the incorporation of clinical knowledge of anatomical structures. Finally, the proposed model permits information from cross-correlated (spatially matched) structural images (e.g. magnetic resonance or computed tomography images) to be incorporated into the reconstruction of functional ECT images. Importantly, this structural information need not be exactly specified: the statistical model permits shifts of predetermined boundaries according to the observed data likelihood function. An important aspect in the clinical evaluation of ECT images is the degree of certainty with which image features have been determined. The statistical model developed in this project permits direct assessment of such image uncertainty. Thus, statistical tests for the existence of tumors are available and confidence intervals for tumor activities and volumes can be constructed.