The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) yields improved image properties. However, implementing the cutting-edge model-based deep learning methods for TOF-PET reconstruction is challenging due to the substantial memory requirements. In this study, we present a novel model-based deep learning approach, LMPDNet, for TOF-PET reconstruction from list-mode data. We address the issue of real-time parallel computation of the projection matrix for list-mode data, and propose an iterative model-based module that utilizes a dedicated network model for list-mode data. Our experimental results indicate that the proposed LMPDNet outperforms traditional iteration-based TOF-PET list-mode reconstruction algorithms. Additionally, we compare the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.

翻译：在正电子发射断层扫描（PET）重建过程中引入飞行时间（TOF）信息可改善图像质量。然而，由于巨大的内存需求，将前沿的基于模型的深度学习方法应用于TOF-PET重建面临挑战。本研究提出一种新颖的基于模型的深度学习方法LMPDNet，用于从列表模式数据进行TOF-PET重建。我们解决了列表模式数据投影矩阵的实时并行计算问题，并提出一种利用专用网络模型处理列表模式数据的迭代式基于模型模块。实验结果表明，所提出的LMPDNet优于传统的基于迭代的TOF-PET列表模式重建算法。此外，我们比较了列表模式数据与正弦图数据在基于模型的深度学习方法中的时空消耗，证明了列表模式数据在基于模型的TOF-PET重建中的优越性。