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Background <p>Coronary artery disease (CAD) is a leading global cause of mortality. Coronary computed tomography angiography (CCTA) is a widely used non-invasive imaging modality for diagnosing CAD and assessing atherosclerotic plaques. Deep learning-based image reconstruction (DLR) has emerged as an advanced technique with the potential to enhance image quality and reduce radiation dose compared to conventional reconstruction methods.</p> Main text <p>This systematic review aimed to evaluate DLR’s effects on image quality and radiation dose in CCTA, comparing it to iterative reconstruction (IR) and filtered back projection (FBP). We conducted a systematic literature search across PubMed, Web of Science, Science Direct, and Embase databases, including articles up to February 2024. After screening 281 initially identified studies, 11 met our predefined inclusion criteria and underwent critical appraisal using the JBI Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. We extracted and analyzed data on image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Our findings indicate that DLR significantly improves image quality by reducing noise and increasing both SNR and CNR when compared to IR and FBP. Moreover, DLR enables radiation dose reduction while maintaining diagnostic image quality.</p> Conclusion <p>Deep learning-based image reconstruction demonstrates substantial potential for simultaneously improving image quality and reducing radiation dose in CCTA. However, further comparative studies are crucial to fully evaluate its impact on diagnostic accuracy and optimize its clinical implementation.</p>

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Image quality and radiation dose in coronary CT: a systematic review and meta-analysis of deep learning reconstruction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Alireza Soleymanzadeh,
  • Fatemeh Salehnia,
  • Davod Khezerlo,
  • Mona Fazel Ghaziyani

摘要

Background

Coronary artery disease (CAD) is a leading global cause of mortality. Coronary computed tomography angiography (CCTA) is a widely used non-invasive imaging modality for diagnosing CAD and assessing atherosclerotic plaques. Deep learning-based image reconstruction (DLR) has emerged as an advanced technique with the potential to enhance image quality and reduce radiation dose compared to conventional reconstruction methods.

Main text

This systematic review aimed to evaluate DLR’s effects on image quality and radiation dose in CCTA, comparing it to iterative reconstruction (IR) and filtered back projection (FBP). We conducted a systematic literature search across PubMed, Web of Science, Science Direct, and Embase databases, including articles up to February 2024. After screening 281 initially identified studies, 11 met our predefined inclusion criteria and underwent critical appraisal using the JBI Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. We extracted and analyzed data on image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Our findings indicate that DLR significantly improves image quality by reducing noise and increasing both SNR and CNR when compared to IR and FBP. Moreover, DLR enables radiation dose reduction while maintaining diagnostic image quality.

Conclusion

Deep learning-based image reconstruction demonstrates substantial potential for simultaneously improving image quality and reducing radiation dose in CCTA. However, further comparative studies are crucial to fully evaluate its impact on diagnostic accuracy and optimize its clinical implementation.