ArticleJournal of the Korean Society of Radiology2023
Adaptation of Deep Learning Image Reconstruction for Pediatric Head CT: A Focus on the Image Quality.
Article in Journal of the Korean Society of Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Comparison of deep learning reconstruction and adaptive statistical iterative reconstruction for head CT in acute stroke.Neuroradiology · 2026Article
- Advancements in radiation dose reduction for pediatric CT head Imaging: A scoping review of emerging Technologies, Protocols, and optimization strategies.Technical innovations & patient support in radiation oncology · 2026Review
- Dose-Dependent Analysis of Image Quality in Pediatric Head CT Scans Across Different Scanners to Optimize Clinical Protocols Using Phantom-Based Assessment.Tomography (Ann Arbor, Mich.) · 2025Article
- Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.Tomography (Ann Arbor, Mich.) · 2024Article
- Article
- Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality.Tomography (Ann Arbor, Mich.) · 2023Article
- Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination.Tomography (Ann Arbor, Mich.) · 2023Article
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Authors and funding
4 authors.
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Abstract
Purpose: To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods: We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results: The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR ( Conclusion: Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.
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