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Reducing radiation dose in routine CT scans: an AI-driven approach with deep learning–based dual-energy CT reconstruction

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The Original Article was published on 02 August 2023

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References

  1. Lyu PJ, Li Z, Chen Y et al (2023) Deep learning reconstruction CT for liver metastases: low dose dual-energy vs standard dose single-energy. Eur Radiol. https://doi.org/10.1007/s00330-023-10033-3

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  2. Bae JS, Lee JM, Kim SW et al (2023) Low-contrast-dose liver CT using low monoenergetic images with deep learning-based denoising for assessing hepatocellular carcinoma: a randomized controlled noninferiority trial. Eur Radiol 33:4344–4354

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Authors and Affiliations

  1. Department of Radiology, Jiangmen Central Hospital, Guangdong Medical University, Zunyi Medical University, 23 Beijie Haibang Street, Jiangmen, 529030, People’s Republic of China

    Enming Cui

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  1. Enming Cui
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Correspondence to Enming Cui.

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The scientific guarantor of this publication is Enming Cui.

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The author of this manuscript declares no relationships with any companies, whose products or services may be related to the subject matter of the article.

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Cui, E. Reducing radiation dose in routine CT scans: an AI-driven approach with deep learning–based dual-energy CT reconstruction. Eur Radiol 34, 26–27 (2024). https://doi.org/10.1007/s00330-023-10066-8

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  • DOI: https://doi.org/10.1007/s00330-023-10066-8

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