PHYSICAL LIMITS OF QUANTITATIVE SPECT

定量光谱的物理极限

• 批准号: 2269853
• 负责人: MARIE FOLEY KIJEWSKI
• 金额: $ 29.57万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-08-01 至 1997-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2269853
• 关键词: Alzheimer's disease; brain disorder diagnosis; brain disorders; diagnosis design /evaluation; diagnosis quality /standard; human subject; image processing; mathematical model; phantom model; physics; single photon emission computed tomography

The proposed research will compare several scatter- and attenuation- correction methods on the basis of their effects on performance in quantitative imaging tasks using SPECT data. The imaging tasks to be considered include estimation of activity and size of spherical structures, estimation of activity within deep brain structures using boundary information derived from MR data, and classification of brain images into normal and Alzheimer's disease groups using cortical activity patterns and activity within deep brain structures. A measure of performance in each quantitative imaging task will be obtained for each correction method being evaluated. These measures will range from theoretical quantities, i.e., the Cramer-Rao lower bound on variance of parameter estimates, to experimentally measured quantities, i.e., standard error of activity values estimated from simulated or phantom images using maximum-likelihood or Bayesian techniques, to clinical quantities, i.e., are under the ROC curve of a statistical or neural network classifier Performance in these quantitative imaging tasks can be viewed as a measure of image quality and is, therefore, a relevant basis on which to compare scatter- and attenuation-correction methods. The proposed research will achieve several objectives beyond the major goal of comparison of correction methods. First, we will investigate the potential of nonuniform projection sampling to improve task performance by reducing noise levels at central locations. Second, we will address the use inn estimation of a priori information, and will evaluate a practical method of estimating deep structure activity using MR-derived boundary information. Progress in these first two areas is expected to lead to improved quantitation of activity in deep brain structures. Finally, we will apply neural networks, which are only beginning to be used in radiology and nuclear medicine, to the Alzheimer's disease- related classification tasks.

拟议的研究将比较几个散射和衰减- 修正方法的基础上，其对性能的影响 定量成像任务使用SPECT数据。 成像任务是 考虑包括估计活动和大小的球形 结构，估计大脑深层结构内的活动， 从MR数据导出的边界信息和脑的分类 正常组和阿尔茨海默病组的大脑皮层活动 大脑深层结构的模式和活动。 的量度 将获得每个定量成像任务中的性能， 正在评估的校正方法。 这些措施将包括 理论量，即，方差Cramer-Rao下界 参数估计，实验测量的量，即， 根据模拟或体模估计的活度值的标准误差 图像使用最大似然或贝叶斯技术，以临床 数量，即，在统计或神经网络的ROC曲线下， 网络分类器在这些定量成像任务中的性能可以 被视为图像质量的衡量标准，因此， 在此基础上比较散射和衰减校正方法。 拟议的研究将实现几个目标以外的主要 校正方法的比较目标。 首先，我们将调查 非均匀投影抽样对提高任务绩效的潜力 降低中心位置的噪音水平。 第二，我们将解决 使用先验信息的内部估计，并将评估 利用MR导出的估计深部构造活动性的实用方法 边界信息 预计在前两个领域取得的进展将 从而改善了对深部脑结构中活动的定量。 最后，我们将应用神经网络，这只是开始 用于放射学和核医学，用于阿尔茨海默氏症- 相关分类任务。