PHYSICAL LIMITS OF QUANTITATIVE SPECT

描述：拟议的研究扩展了我们以前的评估工作 使用任务相关标准的散射和衰减校正方法 到包含真实解剖结构的更复杂的模型。 估计 模拟图像和体模图像的性能，通过滤波重建 通过这些方法校正的数据的反投影，将与 从测量数据中获得最佳性能。 这种理想的表现 将由方差的Cramer-Rao下限（CRB）表示 估计模型参数。 CRB值将根据 预期的正弦图，将通过蒙特卡罗（MC）模拟确定。 实际性能和理想性能之间的任何差异都将代表 通过迭代重建可以获得潜在的优势。 我们还将 继续制定以任务为基础的业绩指标，特别是 其适用于低计数条件或包含先验 信息. 性能测量所依据的成像任务 包括与阿尔茨海默病相关临床分类任务，如 以及数学建模的估计任务。 拟议的研究还针对几个目标以外的 校正方法评价的主要目标。 其中第一个是 通过改变数据收集策略来提高估计性能。 一个 上一个项目期间完成的分析表明， 具有中心峰值灵敏度函数的准直器可以显著地 提高任务绩效。 在建议的计划实施期间，我们会 通过体模实验验证这些改进。 我们也在解决 先验信息的价值问题，如多式联运 结构边界的转移，在估算任务中。 我们将评估， 使用临床分类任务，一种方法，在 上一个项目期间，估计结构活动使用这些 信息. 预计在前两个领域取得的进展 重大临床影响。 目前，大脑深层结构，虽然 已知参与痴呆症，很少检查，因为 难以量化其活动。 这些问题与 大脑中心的高噪音水平， 结构，以及定义这些结构的边界的不精确性， 功能性SPECT图像，所提出的 research. 最后，我们将继续开发处理 低信噪比（SNR）下的估计性能，其中CRB为 无法实现。

DESCRIPTION: The proposed research extends our previous work on evaluation of scatter and attenuation correction methods using task-dependent criteria to more complex models incorporating realistic anatomy. Estimation performance from simulated and phantom images, reconstructed by filtered backprojection of data corrected by these methods, will be compared to the best possible performance from the measured data. This ideal performance will be represented by the Cramer-Rao lower bound (CRB) on variance of estimated model parameters. Values of the CRB will be calculated from the expected sinograms, which will be determined by Monte Carlo (MC) simulation. Any difference between actual and ideal performance would represent the potential advantage to be gained by iterative reconstruction. We will also continue our development of task-based figures-of-merit, particularly those which apply to low-count conditions or to tasks which incorporate a priori information. The imaging tasks on which performance measures will be based include clinical classification tasks related to Alzheimer's disease, as well as the mathematically modeled estimation tasks. The proposed research is also directed toward several objectives beyond the major goal of evaluation of correction methods. The first of these is to improve estimation performance by altering the data collection strategy. An analysis completed during the previous project period has shown that collimators with centrally peaked sensitivity functions can significantly improve task performance. During the proposed project period, we will validate these improvements by phantom experiments. We are also addressing the question of the value of a priori information, such as intermodality transfer of structure boundaries, in estimation tasks. We will evaluate, using a clinical classification task, a method, developed during the previous project period, of estimating structure activity using such information. Progress in these first two areas is expected to have significant clinical impact. At present deep brain structures, although known to be involved in dementia, are rarely examined because of difficulties in quantifying their activity. These problems are related to high noise levels at the center of the brain, the small size of the structures, and imprecision of defining boundaries of these structures from the functional SPECT images, the very issues addressed by the proposed research. Finally, we will continue the development of methods to handle estimation performance at low signal-to-noise ratio (SNR), where the CRB is not attainable.