ArticleFrontiers in medicine2026
Image-domain deep learning denoising for low-dose chest CT on a single 128-slice CT platform: a retrospective image-quality assessment.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Image-Domain GAN Denoising for Sn100 kVp Ultra-Low-Dose Chest CT: A Retrospective Paired Image-Quality Study.Diagnostics (Basel, Switzerland) · 2026Article
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Authors and funding
9 authors.
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Abstract
Background: Image-domain deep learning denoising may provide a practical post-processing approach for improving low-dose chest CT image quality on scanners without native deep learning reconstruction. This retrospective study evaluated a vendor-independent image-domain denoising algorithm applied to low-dose chest CT on a single 128-slice CT platform and descriptively compared the resulting image-quality metrics with those obtained using a standard-dose iterative reconstruction protocol. Methods: This retrospective study included 198 patients who underwent unenhanced chest CT and were assigned to a low-dose CT group (LDCT, Results: Compared with low-dose SAFIRE (LD-SAFIRE), LD-AiR significantly reduced image noise and increased signal-to-noise ratio and contrast-to-noise ratio across all evaluated regions (all Conclusion: On the evaluated CT platform, vendor-independent image-domain deep learning denoising improved objective and subjective image quality of low-dose chest CT compared with LD-SAFIRE. These findings suggest that image-domain denoising may have potential utility for image-quality improvement on CT systems without native deep learning reconstruction. Further prospective, within-subject, multicentre studies incorporating lesion-detectability and diagnostic-performance assessment are needed to clarify its clinical applicability.
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