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- W4386596987 abstract "The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) improves image qualities. 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 presented a novel model-based deep learning approach, LMPDNet, for TOF-PET reconstruction from list-mode data. We addressed the issue of real-time parallel computation of the projection matrix for list-mode data, and proposed an iterative model-based module that utilized a dedicated network model for list-mode data. Our experimental results indicated that the proposed LMPDNet outperformed traditional iteration-based TOF-PET list-mode reconstruction algorithms. Additionally, we compared 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." @default.
- W4386596987 created "2023-09-12" @default.
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- W4386596987 date "2023-10-08" @default.
- W4386596987 modified "2023-09-30" @default.
- W4386596987 title "LMPDNET: TOF-PET List-Mode Image Reconstruction Using Model-Based Deep Learning Method" @default.
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- W4386596987 doi "https://doi.org/10.1109/icip49359.2023.10222478" @default.
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