PET 3-DIMENSIONAL DATA QUANTIFICATION

PET 3 维数据量化

• 批准号: 2111193
• 负责人: TERRENCE R OAKES
• 金额: $ 2.07万
• 依托单位: UNIVERSITY OF BRITISH COLUMBIA
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-03-10 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/2111193
• 关键词: PET; DIMENSIONAL; DATA; QUANTIFICATION

The primary goal of the proposed research is to quantitate PET data acquired in 3-D-mode. The outstanding feature of PET is that it enables researchers and clinicians to assign a quantitative value or rate to an in vivo physiological process. These processes include such things as glucose utilization by tumors, blood supply to the heart, or-the ability of the brain to use neurotransmitters. By removing the collimating septa and acquiring PET data in 3D-mode, the sensitivity is increased by a factor of -6, compared to the conventional slice-mode. If 3D PET could be used on a quantitative basis, the 6-fold increase in sensitivity would translate directly to either a 6-fold decrease in patient dose, a 6-fold increase in statistics, or a 6-fold increase in patient throughput. While data can be acquired in 3D-mode today, it is not yet possible to assign a quantitative value to this data. The difficulties in quantitating 3D PET stem from the lack of collimation, which permits a greater number of undesirable scattered gama-rays to be detected. A tradeoff is made: more "true" events are detected, but more scattered events are also detected, which makes quantitation more difficult. The research proposed here seeks to develop methods and algorithms to enable 3D PET quantitation. The specific areas to be addressed include: detector calibration (assigning an accurate muCi/ml tissue to each image pixel); detector normalization (correcting for differing sensitivities of individual detectors); scatter correction: (correcting for the undesirable scattered events); attenuation correction (since radioactive events originating deep in a tissue mass will be scattered more than those from a superficial location); and image reconstruction (transforming sinogram data into a recognizable image).

这项研究的主要目标是量化PET数据 在3D模式下获得。 PET的突出特点是， 研究人员和临床医生分配一个定量值或率， 体内生理过程。这些过程包括以下内容 肿瘤对葡萄糖的利用，心脏的血液供应，或 大脑使用神经递质的能力通过移除准直隔片 以及在3D模式下采集PET数据，灵敏度增加了 系数为-6，与传统切片模式相比。如果3D PET可以 在定量基础上使用，灵敏度增加6倍， 直接转化为患者剂量减少6倍， 统计数据增加，或患者吞吐量增加6倍。而 数据可以在3D模式下采集，但还不可能分配 这一数据的定量价值。量化3D的困难 PET源于缺乏准直，这允许更大数量的 将检测到不期望的散射伽马射线。一个权衡是：更多 检测到“真”事件，但也检测到更多分散的事件， 这使得定量更加困难。 这里提出的研究旨在开发方法和算法， 启用3D PET定量。需要处理的具体领域包括： 检测器校准（将精确的muCi/ml组织分配给每个图像 像素）;检测器归一化（针对不同灵敏度进行校正 单个探测器）;散射校正：（校正 不良散射事件）;衰减校正（由于放射性 起源于组织块深处的事件将被分散得比 来自浅表位置的那些）;以及图像重建 （将正弦图数据转换成可识别的图像）。