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.

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