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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2302.10481 (eess)
[Submitted on 21 Feb 2023]

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

Authors:Chenxu Li, Rui Hu, Jianan Cui, Huafeng Liu
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Abstract: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.
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2302.10481 [eess.IV]
  (or arXiv:2302.10481v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2302.10481

Submission history

From: Chenxu Li [view email]
[v1] Tue, 21 Feb 2023 07:07:29 UTC (299 KB)
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