Image-Domain GAN Denoising for Sn100 kVp Ultra-Low-Dose Chest CT: A Retrospective Paired Image-Quality Study

Background: Ultra-low-dose (ULD) chest CT can reduce radiation exposure but may compromise image quality. We evaluated image-domain generative adversarial network (GAN)-based denoising at low-dose (LD) and ULD levels, focusing on ULD-AiR versus LD-ADMIRE S3. Methods: In this single-center retrospective paired study, 262 participants underwent LD and ULD chest CT on the same scanner. Images reconstructed with Advanced Modeled Iterative Reconstruction at strength 3 (ADMIRE S3) were post-processed with AiR Denoising v1.0, yielding four series. Objective metrics, including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and 5-point subjective ratings were compared within dose levels and between ULD-AiR and LD-ADMIRE S3. Exploratory regression assessed associations of ULD-to-LD SNR log-ratios with the volume CT dose index (CTDIvol) log-ratio and anthropometric variables. Results: The median paired reduction in estimated effective dose from LD to ULD CT was 46.5%. Within each dose level, AiR reduced image noise and increased SNR, CNR, and subjective scores. Compared with LD-ADMIRE S3, ULD-AiR showed lower image noise and higher SNR/CNR in the lung, aorta, and muscle; liver findings were less consistent, whereas vertebral metrics were less favorable. Lung-parenchyma scores did not differ significantly, mediastinal soft-tissue scores favored ULD-AiR, and overall image-noise scores favored LD-ADMIRE S3. Exploratory regression identified CTDIvol log-ratio associations with aortic and muscle SNR log-ratios; some anthropometric associations were sensitive to an influential observation. Conclusions: Image-domain GAN denoising improved several objective and subjective image-quality metrics at both dose levels. With a median paired reduction of 46.5% in estimated effective dose, ULD-AiR showed tissue- and endpoint-specific image-quality differences relative to LD-ADMIRE S3. These findings do not establish diagnostic or screening equivalence.

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Publication Details

Journal
Diagnostics
Published
2026-09-15
DOI
https://doi.org/10.3390/diagnostics16182988
Primary Topic
Radiation Dose and Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Image-Domain GAN Denoising for Sn100 kVp Ultra-Low-Dose Chest CT: A Retrospective Paired Image-Quality Study

Fajin Lv, Xue Jiang, Wang-jia Li, Liang Lv et al.
Diagnostics
Radiation Dose and Imaging
article

Image-Domain GAN Denoising for Sn100 kVp Ultra-Low-Dose Chest CT: A Retrospective Paired Image-Quality Study

Fajin Lv, Xue Jiang, Wang-jia Li, Liang Lv, Guangpeng Zhang, Yang Li, Zhiyuan Zhang, Kaiqing Yao, 郑伊能, Zhiwei Zhang, Xinyou Li
article en

Abstract

Background: Ultra-low-dose (ULD) chest CT can reduce radiation exposure but may compromise image quality. We evaluated image-domain generative adversarial network (GAN)-based denoising at low-dose (LD) and ULD levels, focusing on ULD-AiR versus LD-ADMIRE S3. Methods: In this single-center retrospective paired study, 262 participants underwent LD and ULD chest CT on the same scanner. Images reconstructed with Advanced Modeled Iterative Reconstruction at strength 3 (ADMIRE S3) were post-processed with AiR Denoising v1.0, yielding four series. Objective metrics, including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and 5-point subjective ratings were compared within dose levels and between ULD-AiR and LD-ADMIRE S3. Exploratory regression assessed associations of ULD-to-LD SNR log-ratios with the volume CT dose index (CTDIvol) log-ratio and anthropometric variables. Results: The median paired reduction in estimated effective dose from LD to ULD CT was 46.5%. Within each dose level, AiR reduced image noise and increased SNR, CNR, and subjective scores. Compared with LD-ADMIRE S3, ULD-AiR showed lower image noise and higher SNR/CNR in the lung, aorta, and muscle; liver findings were less consistent, whereas vertebral metrics were less favorable. Lung-parenchyma scores did not differ significantly, mediastinal soft-tissue scores favored ULD-AiR, and overall image-noise scores favored LD-ADMIRE S3. Exploratory regression identified CTDIvol log-ratio associations with aortic and muscle SNR log-ratios; some anthropometric associations were sensitive to an influential observation. Conclusions: Image-domain GAN denoising improved several objective and subjective image-quality metrics at both dose levels. With a median paired reduction of 46.5% in estimated effective dose, ULD-AiR showed tissue- and endpoint-specific image-quality differences relative to LD-ADMIRE S3. These findings do not establish diagnostic or screening equivalence.

DiagnosticsVol. 16(18)
Sino Biological (China) (CN), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Chongqing Emergency Medical Center (CN), State Key Laboratory of Vehicle NVH and Safety Technology (CN), Chongqing Medical University (CN)
Chongqing Science and Technology Commission
Good health and well-being
Openalex Percentile: Top 12%
Radiation Dose and Imaging
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