Statistical PET Image Reconstruction

描述(申请人提供)：正电子发射断层扫描(PET)正在经历一个巨大的发展时期，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方法将在未来许多年内得到扩展。技术进步正在推动PET采用先进的基于统计的重建算法进行全3D成像。非常需要改进的迭代算法，其速度足够快，用于全3D PET的常规使用，并且消除了选择重建参数和正则化方案的猜测工作。该项目的目标是研究统计正电子发射计算机断层扫描重建的新范例，这些重建是专门针对评估和检测任务而分别优化的。提出了两(2)个互补重建框架：(1)利用系统传递矩阵的综合建模从原始LOR直方图直接重建，其利用精确的泊松统计实现真正的最大似然估计，以产生低噪声、更高空间分辨率的图像；(2)统计调节期望最大化(StatREM)算法，其适应被重建数据集的统计质量。StatREM框架提供了一种以统计意义的方式选择子集和加速的方法，提供了比当前算法更健壮的加速。它还提供了迭代停止准则，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，为高统计量区域提供高分辨率，同时正则化低计数背景区域。我们假设StatREM提供了比现有算法更好的病变检测性能。AIMS 3和AIMS 4将分别详细评估新算法的量化和病变检测性能，使用通过实验获得的高度可重复性全身体模的数据。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和多层显示和定位接收器工作特性(LROC)分析来评估病变的可检测性。这项研究提供的图像质量的改善将广泛影响PET成像的所有应用，特别是在肿瘤检测和定量方面。

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.