Statistical PET Image Reconstruction

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时期，新的示踪剂和肿瘤学，心脏病学和神经学应用的持续发展确保了这种模式将在未来许多年扩展。技术进步正在推动PET向全3d成像发展，并采用先进的基于统计的重建算法。有必要改进迭代算法，使其足够快，以适应全三维PET的常规使用，并且在选择重建参数和正则化方案时不需要猜测。该项目的目的是研究统计PET重建的新范式，这些新范式是专门针对估计和检测任务进行优化的。提出了两种互补的重建框架：（目标1）利用系统传递矩阵的综合建模对原始LOR直方图进行直接重建，利用精确的泊松统计量实现真正的最大似然估计，生成低噪声、高空间分辨率的图像；（目标2）统计调节期望最大化（StatREM）算法，该算法适应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强大的加速。它还提供了一个迭代停止准则，可以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和目标4将分别详细评估新算法的定量和病变检测性能，使用实验获得的高可重复性全身幻影数据。每个算法都将针对这些任务进行优化。病变可检测性将通过详细的人体观察者研究、多层显示和定位接收器工作特征（LROC）分析来评估。本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，对肿瘤检测和定量有特殊的好处。

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.