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

LMPDNet: TOF-PET list-mode image reconstruction using model-based deep learning method