Optimized image reconstruction for time-of-flight PET

描述(由申请人提供)：全3D飞行时间(TOF)PET扫描仪为临床PET提供了以前无法实现的信噪比。相对较新的快速闪烁体具有高速、停止功率和光输出的组合，使临床TOF PET实用。因此，在不久的将来，TOF有可能成为临床全身PET的标准。我们将以我们在PET图像重建方法方面的经验和我们在飞行时间数据回弹方面的最新结果为基础，开发优化用于TOF-PET数据的图像重建方法。在过去的十年里，在非TOF PET系统中，我们看到了用于临床研究的方法的进步，从解析重建到基于傅立叶反投影和2D迭代重建的方法，再到全3D迭代重建。这样做的原因是，与其他方法相比，使用所有数据和更精确的模型进行迭代3D重建可以实现更好的性能。同样，在迭代重建框架中使用完整的TOF数据也应该会产生最佳性能。该项目的主要目标是：(I)开发一种优化的全3D TOF重建方法，将我们早期的3D PET的MAP方法扩展到TOF；以及(Ii)系统地研究开发更快的TOF PET重建算法所涉及的权衡，并实施和评估实用方法，其计算成本与其在临床环境中的使用一致。我们将首先开发我们的地图重建方法的TOF扩展，该方法将精确的物理和统计建模与快速收敛算法相结合。空间变化的罚函数将被用来确保计数独立和空间不变的分辨率。然后，我们将以此为基准，与其他简化方法进行比较。其中，我们将研究使用新的傅立叶反投影方法对TOF-PET数据进行快进和反投影，以及使用反反投影将TOF减少到非TOF数据的数据简化方法。这些研究将包括研究TOF-PET数据的数学性质，分析MAP重建的偏差和协方差，以及开发快速计算算法。该项目的结果将是一个用于TOF-PET图像重建的实用软件与对这些方法的特性的分析研究以及对这些重建方法的计算、模型和人体研究评估相结合。 与公众健康相关：新一代PET扫描仪使用时间信息来检测放射性排放，以进一步提高图像质量。这转化为更好地检测癌症患者肿瘤的能力，这反过来又提供了改进治疗计划和结果的潜力。该项目将开发计算方法和软件，以优化这一新一代扫描仪的性能。

DESCRIPTION (provided by applicant): Fully 3D time-of-flight (TOF) PET scanners offer the potential for previously unachievable signal to noise ratios in clinical PET. Relatively new, fast scintillators have the combination of high speed, stopping power and light output, that make clinical TOF PET practical. Consequently it is likely that TOF will become the standard for clinical whole body PET in the near future. We will build on our experience in PET image reconstruction methodology and our recent results on rebinning of time of flight data to develop image reconstruction methods that are optimized for use with TOF-PET data. In non-TOF PET systems we have seen a progression over the past decade in the methods used for clinical studies from analytic reconstruction, to those based on Fourier rebinning and 2D iterative reconstruction, to fully 3D iterative reconstruction. The reason for this is that iterative 3D reconstruction using all of the data and more accurate models can achieve improved performance relative to the other approaches. Similarly, using the full TOF data in an iterative reconstruction framework should also lead to the best performance. The main goals of this project are: (i) to develop an optimized fully 3D TOF reconstruction method that extends our earlier MAP approach for 3D PET to TOF; and (ii) to systematically study the trade-offs involved in developing faster TOF PET reconstruction algorithms and to implement and evaluate practical methods with a computational cost consistent with their use in clinical settings. We will first develop a TOF-extension of our MAP approach to reconstruction that combines accurate physical and statistical modeling with fast convergent algorithms. Spatially variant penalty functions will be used to ensure count independent and spatially invariant resolution. We will then use this as a benchmark against which to compare other simplified methods. Among these we will investigate the use of novel Fourier rebinning methods for fast forward and backprojection of TOF-PET data, as well as data reduction methods in which we use rebinning to reduce TOF to non-TOF data. These investigations will include a study of the mathematical properties of the TOF-PET data, analysis of the bias and covariance of MAP reconstructions, and the development of fast computational algorithms. The result of this project will be a combination of practical software for TOF- PET image reconstruction with analytic studies of the properties of these methods and computational, phantom and human-study evaluation of these reconstruction methods. PUBLIC HEALTH RELEVANCE: The new generation of PET scanners uses timing information in detecting radioactive emissions to further improve image quality. This translates into the ability to better detect tumors in cancer patients, which in turn offers the potential of improved treatment planning and outcomes. This project will develop computational methods and software to optimize the performance of this new generation of scanners.