Optimized image reconstruction for time-of-flight PET

描述（由申请人提供）：全3D飞行时间（TOF）PET扫描仪提供了临床PET中以前无法实现的信噪比。相对较新的快速闪烁器具有高速、停止功率和光输出的组合，使临床TOF PET实用化。因此，TOF很可能在不久的将来成为临床全身PET的标准。我们将基于我们在PET图像重建方法学方面的经验和我们最近在飞行时间数据重组方面的结果，开发针对TOF-PET数据进行优化的图像重建方法。在非TOF PET系统中，我们已经看到了过去十年中用于临床研究的方法的进展，从分析重建到基于傅立叶重组和2D迭代重建的方法，再到完全3D迭代重建。其原因是使用所有数据和更精确的模型的迭代3D重建可以实现相对于其他方法的改进的性能。类似地，在迭代重建框架中使用完整TOF数据也应该导致最佳性能。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 MAP方法扩展到TOF;以及（ii）系统地研究开发更快TOF PET重建算法所涉及的权衡，并实施和评估计算成本与其在临床环境中使用一致的实用方法。我们将首先开发我们的MAP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。空间变化罚函数将用于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，比较其他简化方法。其中，我们将研究使用新的傅立叶重组方法快速向前和向后投影的TOF-PET数据，以及数据减少的方法，我们使用重组减少TOF非TOF数据。这些调查将包括TOF-PET数据的数学性质的研究，MAP重建的偏差和协方差的分析，以及快速计算算法的开发。该项目的结果将是TOF-PET图像重建的实用软件与这些方法的属性的分析研究以及这些重建方法的计算、体模和人体研究评估的结合。 公共卫生相关性：新一代PET扫描仪在检测放射性发射时使用定时信息，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，从而提供了改善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.