ReviewTechnical innovations & patient support in radiation oncology2026
Advancements in radiation dose reduction for pediatric CT head Imaging: A scoping review of emerging Technologies, Protocols, and optimization strategies.
Review in Technical innovations & patient support in radiation oncology, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Rethinking surveillance imaging to reduce radiation exposure among survivors of childhood cancer.Pediatric radiology · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computed tomography (CT) is an important imaging modality that provides cross-sectional images, aiding in the detailed visualization of internal structures for accurate diagnosis and treatment. The pediatric population is more sensitive to radiation than adults, making radiation dose (RD) optimization an important concern in pediatric CT imaging. This scoping review emphasizes advanced RD reduction methods used in pediatric CT head imaging for diagnosing various clinical conditions with optimum RD and diagnostic image quality (IQ). A detailed search was conducted across five databases, such as PubMed, Scopus, CINAHL, Web of Science (WOS) and Embase using relevant keywords. A total of 24 articles were included in the final review. RD parameters and IQ related data were extracted from each article. Conventional RD reduction techniques in CT such as reducing the tube voltage, tube current and other scanning parameters, face limitations particularly in the pediatric population. These techniques lead to a trade-off between a lower RD and poor IQ which might obfuscate diagnostic details due to decreased contrast resolution with greater image noise and artifacts. To balance RD and diagnostic IQ, advanced technologies such as iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms with ultra-low dose protocols are increasingly being used. Hence, the review concludes that, compared with conventional dose reduction techniques, artificial intelligence based DLIR algorithms enhance IQ even for ultra-low dose protocols across various clinical domains in pediatric CT imaging.
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