Image-domain deep learning denoising for low-dose chest CT on a single 128-slice CT platform: a retrospective image-quality assessment

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, n = 99) or a standard-dose CT group (SDCT, n = 99) according to the clinical acquisition protocol. All images were reconstructed using sinogram-affirmed iterative reconstruction (SAFIRE). LDCT images were additionally processed with a vendor-independent image-domain deep learning denoising algorithm (AiR Denoising), generating low-dose AiR-denoised images (LD-AiR). Objective image quality was assessed using attenuation, image noise, signal-to-noise ratio, and contrast-to-noise ratio. Subjective image quality of the lung parenchyma and mediastinal soft tissue was independently evaluated by readers using a 5-point scale. 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 p < 0.05). Subjective image quality scores for both lung parenchyma and mediastinal soft tissue were also significantly higher with LD-AiR than with LD-SAFIRE (all p < 0.05). In the descriptive between-group comparison, the LDCT protocol was associated with an approximately 76% lower effective dose than the SDCT protocol. Most objective and subjective image-quality metrics of LD-AiR did not differ significantly from those of SD-SAFIRE; however, this comparison was based on different patient groups and should not be interpreted as evidence of equivalence or non-inferiority. 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.