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Updated: Jan 23, 2026

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
Published on: September 11, 2011
Advancements in radiation dose reduction for pediatric CT head Imaging: A scoping review of emerging Technologies,
Nandini B Patil1, Priyanka1, Priya P S2
1Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.
Insights
Advanced artificial intelligence algorithms significantly improve pediatric CT imaging quality even at ultra-low radiation doses. This offers better diagnostic accuracy while minimizing radiation exposure for children undergoing computed tomography (CT) scans.
Area of Science:
- Medical Imaging
- Radiology
- Pediatric Imaging
Background:
- Computed tomography (CT) is crucial for pediatric diagnosis, but radiation dose (RD) optimization is vital due to increased sensitivity in children.
- Conventional RD reduction methods in pediatric CT often compromise image quality (IQ), leading to noise and artifacts.
Purpose of the Study:
- To review advanced RD reduction methods in pediatric CT head imaging.
- To evaluate techniques balancing RD and diagnostic IQ for pediatric patients.
Main Methods:
- A scoping review of 24 articles from five major databases (PubMed, Scopus, CINAHL, Web of Science, Embase).
- Extraction of RD parameters and IQ data from selected studies.
Main Results:
- Conventional RD reduction techniques in pediatric CT present a trade-off between lower RD and reduced IQ.
- Advanced technologies like iterative reconstruction (IR) and deep learning image reconstruction (DLIR) are increasingly used with ultra-low dose protocols.
- AI-based DLIR algorithms demonstrate enhanced IQ at ultra-low doses compared to conventional methods.
Conclusions:
- AI-based DLIR algorithms significantly improve image quality in ultra-low dose pediatric CT imaging.
- DLIR offers a superior alternative to conventional dose reduction techniques for pediatric head CT.
- Advanced algorithms are essential for optimizing RD and maintaining diagnostic IQ in pediatric CT.
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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