Statistical PET Image Reconstruction

统计 PET 图像重建

• 批准号: 6920909
• 负责人: Dan J Kadrmas
• 金额: $ 16.07万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-16 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6920909
• 关键词: bioimaging /biomedical imaging; clinical research; computer program /software; computer simulation; digital imaging; evaluation /testing; human data; image enhancement; mathematical model; model design /development; neoplasm /cancer diagnosis; phantom model; positron emission tomography; statistics /biometry; three dimensional imaging /topography

DESCRIPTION (provided by applicant): Positron emission tomography (PET) is undergoing a period of tremendous growth, and the continued development of new tracers and applications for oncology, cardiology, and neurology ensures that this modality will expand for many years to come. Technological advances are pushing PET toward fully-3D imaging with advanced statistical-based reconstruction algorithms. There is a significant need for improved iterative algorithms which are fast enough for routine use with fully-3D PET, and which take the guesswork out of choosing reconstruction parameters and regularization schemes. The objective of this project is to investigate new paradigms for statistical PET reconstruction which are specifically targeted and separately optimized for estimation and detection tasks. Two (2) complementary reconstruction frameworks are proposed: (Aim 1) direct reconstruction from raw LOR histograms using comprehensive modeling of the system transfer matrix, which achieves true maximum-likelihood estimation with exact Poisson statistics to produce lower-noise, higher spatial resolution images; and (Aim 2) statistically-regulated expectation-maximization (StatREM) algorithms, which adapt to the statistical quality of the dataset being reconstructed. The StatREM framework provides a means for selecting subsets and acceleration in a statistically-meaningful way, offering more robust acceleration than current algorithms. It also provides an iterative stopping criterion which may be optimized specifically for estimation and detection tasks. Moreover, StatREM provides spatially-adaptive regularizations which offer high resolution for high statistics regions, while at the same time regularizing low count background regions. We hypothesize that StatREM provides better lesion detection performance than current algorithms. Aims 3 and 4 will evaluate in detail the quantitation and lesion detection performance, respectively, of the new algorithms using experimentally acquired data of a highly-reproducible whole-body phantom. Each algorithm will be optimized with respect to these tasks. Lesion detectability will be evaluated using a detailed human observer study with a multi-slice display and localization receiver operating characteristic (LROC) analysis. The improvements in image quality offered by this research will broadly impact all applications of PET imaging, with specific benefit for tumor detection and quantitation.

描述（由申请人提供）：正电子发射断层扫描（PET）正在经历一个巨大的增长期，新示踪剂的持续开发以及肿瘤学、心脏病学和神经病学的应用确保了这种模式将在未来许多年内得到扩展。技术进步正在推动PET采用先进的基于解剖学的重建算法实现全3D成像。有一个显着的需要，改进的迭代算法是足够快的常规使用全三维PET，并采取了猜测选择重建参数和正则化方案。本项目的目的是研究统计PET重建的新范例，这些新范例针对估计和检测任务进行专门针对性和单独优化。提出了两种互补的重建框架：（目的1）使用系统传递矩阵的综合建模从原始LOR直方图直接重建，其实现具有精确泊松统计的真实最大似然估计以产生低噪声、高空间分辨率图像;和（目标2）适应于被重建的数据集的统计质量的预测调节期望最大化（StatREM）算法。StatREM框架提供了一种方法来选择子集和加速在一个有意义的方式，提供更强大的加速比目前的算法。它还提供了一种迭代停止准则，该准则可以专门针对估计和检测任务进行优化。此外，StatREM提供空间自适应正则化，其为高统计区域提供高分辨率，同时正则化低计数背景区域。我们假设StatREM提供比当前算法更好的病变检测性能。目标3和4将分别使用高度可再现的全身体模的实验采集数据详细评价新算法的定量和病变检测性能。每个算法都将针对这些任务进行优化。将使用详细的人类观察者研究以及多切片显示和定位受试者工作特征（LROC）分析来评价病变可检测性。这项研究提供的图像质量的改进将广泛影响PET成像的所有应用，特别是肿瘤检测和定量。