RECONSTRUCTION AND ANALYSIS OF EMISSION TOMOGRAPHY DATA

发射型计算机断层扫描（ECT）是一种重要的工具，在检测 病变和在体内生理功能的研究。 因此 在肿瘤的定位和确定肿瘤的 转移状态 它还有助于开发新的 需要量化代谢过程的医疗技术， 示例性单克隆抗体方案。 该项目的目标是 最大化从ECT数据中提取的诊断信息量， 完成这些临床任务。 实现这一目标的机制是 生成ECT数据的物理过程的统计模型， 其相关的源分布。 统计模型开发于 这个项目包含了与肿瘤直接相关的参数， 检测和定位。 根据观测值估计这些参数 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图像自动分割成 活动区分区域。 它还允许临床医生进行交互， ECT图像的重建，从而提高了结果的准确性 重建通过纳入临床知识， 解剖结构 最后，该模型允许信息 从互相关的（空间匹配的）结构图像（例如磁 共振或计算机断层摄影图像）被结合到 功能性ECT图像的重建。 重要的是，这种结构 信息不需要精确地指定：统计模型允许 根据观测数据的预定边界的偏移 似然函数 ECT图像临床评价的一个重要方面是 确定图像特征的确定性。 的 本项目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 肿瘤可用，肿瘤活性的置信区间， 可以构建卷。

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.