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：将光瞬时信息纳入光学发射光学(PET)重建过程中的光线信息整合到光谱射线成像学(PET)的重建过程中,可产生更好的图像特性。然而,由于大量的记忆要求,实施以模型为基础的尖端深层学习方法对TOF-PET重建具有挑战性。在本研究中,我们提出了一个基于模型的深层次学习方法,即LMPDNet,用于从列表模式数据中重建TOF-PET。我们处理的是列表模式数据预测矩阵的实时平行计算问题,并提出了一个基于迭代模型的模块,该模块利用了列表模式数据专用网络模型。我们的实验结果表明,拟议的LMPDNet比传统的基于TOF-PET列表模型重建算法更符合传统循环法。此外,我们比较了基于模型的深深层学习方法中列表模式数据的空间和时间消耗量和罪状图数据,展示了基于模型的TOF-PET重建中列表模式数据的优越性。