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Bridging physics and intelligence: The evolutionary landscape of PET image reconstruction

  • 1.

    Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

  • 2.

    Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, State Key Laboratory of Biomedical Imaging Science and System, Shenzhen 518055, China

  • 3.

    Lab of Molecular Imaging and Medical Intelligence, Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen 518116, China

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  • Corresponding author: zl.hu@siat.ac.cn
  • Received Date:  January 23, 2026
    Accepted Date:  June 02, 2026
    Published Online:  June 04, 2026
The Innovation Informatics  https://doi.org/10.59717/j.xinn-inform.2026.100052  |  Cite this article
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  • [1] Bousse A., Kandarpa V.S.S., Shi K., et al. (2024). A review on low-dose emission tomography post-reconstruction denoising with neural network approaches. IEEE Trans. Radiat. Plasma Med. Sci. 8:333−347. DOI:10.1109/TRPMS.2023.3349194

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    [4] Sun M., Yang Z., Huang Y., et al. (2025). Federated learning for large models in medical imaging: A comprehensive review. arXiv preprint. DOI: arXiv:2508.20414

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    [5] Huang B., Chen K., Li B., et al. (2025). ALL-PET: A low-resource and low-shot PET foundation model in projection domain. arXiv preprint. DOI: arXiv: 2509.09130

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  • Cite this article:

    Jiang H., Guo W., Zhang Q., et al. (2026). Bridging physics and intelligence: The evolutionary landscape of PET image reconstruction. The Innovation Informatics 2:100052. https://doi.org/10.59717/j.xinn-inform.2026.100052
    Jiang H., Guo W., Zhang Q., et al. (2026). Bridging physics and intelligence: The evolutionary landscape of PET image reconstruction. The Innovation Informatics 2:100052. https://doi.org/10.59717/j.xinn-inform.2026.100052

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