ArticleTomography (Ann Arbor, Mich.)2022
AI Denoising Improves Image Quality and Radiological Workflows in Pediatric Ultra-Low-Dose Thorax Computed Tomography Scans.
Article in Tomography (Ann Arbor, Mich.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Performance of Commercial Deep Learning-Based Radiation Dose Optimization Software in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2026Review
- Attenuation-based ultra-low-dose lung computed tomography at 0.1 mSv to 0.3 mSv effective dose in children.Pediatric radiology · 2026Article
- The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.Children (Basel, Switzerland) · 2025Review
- Enhancing Image Quality in Dental-Maxillofacial CBCT: The Impact of Iterative Reconstruction and AI on Noise Reduction-A Systematic Review.Journal of clinical medicine · 2025Review
- Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study.Breast cancer research : BCR · 2025Article
- The Effect of Simulated Dose Reduction on the Performance of Artificial Intelligence in Chest Radiography.Journal of imaging · 2025Article
- Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.Scientific reports · 2024Article
- Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen.Diagnostics (Basel, Switzerland) · 2024Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- Milestones in CT: Past, Present, and Future.Radiology · 2023Review
- Deep-Learning-Based Image Denoising in Imaging of Urolithiasis: Assessment of Image Quality and Comparison to State-of-the-Art Iterative Reconstructions.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms.Seminars in roentgenology · 2023Article
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Authors and funding
13 authors.
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Abstract
(1) This study evaluates the impact of an AI denoising algorithm on image quality, diagnostic accuracy, and radiological workflows in pediatric chest ultra-low-dose CT (ULDCT). (2) Methods: 100 consecutive pediatric thorax ULDCT were included and reconstructed using weighted filtered back projection (wFBP), iterative reconstruction (ADMIRE 2), and AI denoising (PixelShine). Place-consistent noise measurements were used to compare objective image quality. Eight blinded readers independently rated the subjective image quality on a Likert scale (1 = worst to 5 = best). Each reader wrote a semiquantitative report to evaluate disease severity using a severity score with six common pathologies. The time to diagnosis was measured for each reader to compare the possible workflow benefits. Properly corrected mixed-effects analysis with post-hoc subgroup tests were used. Spearman’s correlation coefficient measured inter-reader agreement for the subjective image quality analysis and the severity score sheets. (3) Results: The highest noise was measured for wFBP, followed by ADMIRE 2, and PixelShine (76.9 ± 9.62 vs. 43.4 ± 4.45 vs. 34.8 ± 3.27 HU; each p < 0.001). The highest subjective image quality was measured for PixelShine, followed by ADMIRE 2, and wFBP (4 (4−5) vs. 3 (4−5) vs. 3 (2−4), each p < 0.001) with good inter-rater agreement (r ≥ 0.790; p ≤ 0.001). In diagnostic accuracy analysis, there was a good inter-rater agreement between the severity scores (r ≥ 0.764; p < 0.001) without significant differences between severity score items per reconstruction mode (F (5.71; 566) = 0.792; p = 0.570). The shortest time to diagnosis was measured for the PixelShine datasets, followed by ADMIRE 2, and wFBP (2.28 ± 1.56 vs. 2.45 ± 1.90 vs. 2.66 ± 2.31 min; F (1.000; 99.00) = 268.1; p < 0.001). (4) Conclusions: AI denoising significantly improves image quality in pediatric thorax ULDCT without compromising the diagnostic confidence and reduces the time to diagnosis substantially.
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