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Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening allergic-like reactions, and environmental contamination. Deep learning (DL) models can generate full-dose ICM CT images from non-contrast or low-dose ICM administration or generate non-contrast CT from full-dose ICM CT. Eliminating the need for both contrast-enhanced and non-enhanced imaging or reducing the amount of required contrast while maintaining diagnostic capability may reduce overall patient risk, improve efficiency and minimize costs. We reviewed the current capabilities of DL to reduce the need for contrast administration in CT.

Methods

We conducted a systematic review of articles utilizing DL to reduce the amount of ICM required in CT, searching MEDLINE, Embase, Compendex, Inspec, and Scopus to identify papers published from 2016 to 2022. We classified the articles based on the DL model and ICM reduction.

Results

Eighteen papers met the inclusion criteria for analysis. Of these, ten generated synthetic full-dose (100%) ICM from real non-contrast CT, while four augmented low-dose to full-dose ICM CT. Three used DL to create synthetic non-contrast CT from real 100% ICM CT, while one paper used DL to translate the 100% ICM to non-contrast CT and vice versa. DL models commonly used generative adversarial networks trained and tested by paired contrast-enhanced and non-contrast or low ICM CTs. Image quality metrics such as peak signal-to-noise ratio and structural similarity index were frequently used for comparing synthetic versus real CT image quality.

Conclusion

DL-generated contrast-enhanced or non-contrast CT may assist in diagnosis and radiation therapy planning; however, further work to optimize protocols to reduce or eliminate ICM for specific pathology is still needed along with a dedicated assessment of the clinical utility of these synthetic images.

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Acknowledgements

We would like to acknowledge the kind assistance of Li Zhang, MLIS, for creating and running the literature search string, and Ekta Walia, Ph.D. for proofreading the article. We gratefully acknowledge funding support for this research from the Saskatchewan Health Research Foundation. Figures 2 and 3 of this manuscript are created using BioRender.

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Authors and Affiliations

  1. Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK, Canada

    Ghazal Azarfar & Paul S. Babyn

  2. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada

    Ghazal Azarfar & Seok-Bum Ko

  3. Department of Radiology, Stanford University, Stanford, CA, USA

    Scott J. Adams

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  1. Ghazal Azarfar
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Correspondence to Ghazal Azarfar.

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Azarfar, G., Ko, SB., Adams, S.J. et al. Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review. Int J CARS 18, 1903–1914 (2023). https://doi.org/10.1007/s11548-023-02862-w

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