ESTIMATION STRATEGIES FOR NUCLEAR MEDICAL IMAGING

描述（改编自申请人的摘要）： 本研究旨在开发体内定量的技术， 放射性同位素在小动物中的定位，以加速和 简化新的放射性标记药物的设计和评价。 作为另一个项目的一部分，申请人正在完成 高分辨率单光子环形断层成像仪的构建 SPRINT后，但专门设计用于成像99 mTc，123 I， 131 I在小鼠和大鼠中的毫米分辨率。 申请人建议使用统一的Cramer-Rao（CR）下限 作为一种工具，以评估从一个 成像系统，并比较几种估计器的性能， 确定整个器官或肿瘤的特定任务目标 器官内的活性浓度和分布。 初始 应用和测试将是小动物的情况下，但它 预计开发的方法将适用于 人类的形象。 将开发计算CR界限的方法 当费歇尔矩阵条件不好时。 若干措施 将从偏置图像导出的偏移梯度长度与偏置梯度长度进行比较 以确定它们作为图像质量衡量标准的有用性。 的 将使用均匀的CR边界来表征成像系统 性能、估计器性能和系统的鲁棒性， 对象模型 两个相对较新的估计算法，惩罚 加权最小二乘（PWLS）和空间交替广义 期望最大化（SAGE）将被评估和比较， 为量化任务确定的性能限值， 辅助信息可用的情况，例如以估计的形式 从核磁共振获得的器官和肿瘤边界 (NMR)图像和没有这样的信息是可用的。 方法将 被设计为从NMR图像参数化边界，构建 用于联合估计边界和比活度的估计器，以及 描述这些估计值的偏差和方差。 理论 将通过模拟、幻影和小动物测试这些发展 成像实验

DESCRIPTION (Adapted from Applicant's Abstract): The long term aim of this research is to develop techniques for in vivo quantification of radioisotope localization in small animals in order to speed and simplify the design and evaluation of new radiolabeled pharmaceuticals. As part of another project, the applicants are completing the construction of a high-resolution single photon ring tomograph modeled after SPRINT, but specifically designed for imaging 99mTc, 123I, and 131I in mice and rats with millimeter resolution. The applicant have proposed to use uniform Cramer-Rao (CR) lower bound as a tool to evaluate the intrinsic quality of data obtained from an imaging system, and to compare the performance of several estimators for the task- specific objectives of determining whole organ or tumor activity concentration and distribution within the organ. The initial application and testing will be for the case of small animals, but it is anticipated that the methods developed will be applicable to the imaging of humans. Methods will be developed to compute the CR bound when the Fisher matrices are poorly conditioned. Several measures derived from the bias image will be compared to the bias gradient length to determine their usefulness as a measure of image quality. The uniform CR bound will be used to characterize imaging system performance, estimator performance, and the robustness of the system and object models. Two relatively recent estimation algorithms, Penalized Weighted Least Squares, (PWLS), and Space-Alternating Generalized Expectation- maximization, (SAGE), will be evaluated and compared, and performance limits determined for the quantification tasks both for the case where side information is available, e.g. in the form of estimates of organ and tumor boundaries obtained from nuclear magnetic resonance (NMR) images and were no such information is available. Methods will be devised to parameterize boundaries from NMR images, construct estimators for joint estimation of boundaries and specific activity, and characterize the bias and variance of these estimates. Theoretical developments will be tested by simulation and phantom and small animal imaging experiments.