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Image quality comparison between low-dose thin-slice deep-learning reconstruction and standard-dose thick-slice

Ryota Harai1, Yasunori Nagayama1, Soichiro Ishiuchi1

  • 1Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.

European Journal of Radiology
|February 4, 2026
PubMed

Insights

Low-dose thin-slice deep-learning reconstruction (DLR) in pediatric abdominal CT significantly enhances image quality, improving edge sharpness and diagnostic confidence. This advanced technique offers superior visualization without increasing noise compared to standard-dose methods.

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Pediatric abdominal CT scans often involve a trade-off between radiation dose and image quality.
  • Standard-dose thick-slice hybrid iterative reconstruction (HIR) is commonly used but may not optimize visualization of small structures.
  • Low-dose protocols are desirable to reduce radiation exposure in children.

Purpose of the Study:

  • To compare the image quality of low-dose thin-slice deep-learning reconstruction (DLR) with standard-dose thick-slice hybrid iterative reconstruction (HIR) in pediatric abdominal CT.
  • To evaluate the impact of DLR on image noise, contrast-to-noise ratio (CNR), edge sharpness, and diagnostic confidence.

Main Methods:

  • Retrospective analysis of contrast-enhanced abdominal CT scans from 82 children (≤6 years) using standard-dose (STD) or low-dose (LD) protocols.
  • Images were reconstructed using HIR at 3.0-mm (STD-HIR/3.0, LD-HIR/3.0) and LD images also at 0.5-mm using HIR (LD-HIR/0.5) and DLR (LD-DLR/0.5).
  • Quantitative analysis included size-specific dose estimate (SSDE), image noise, CNR, noise power spectrum (NPS), and edge-rise slope (ERS); subjective evaluation assessed noise, sharpness, structure delineation, and diagnostic confidence.

Main Results:

  • Low-dose protocols achieved a 59.5% reduction in SSDE compared to standard-dose.
  • LD-DLR/0.5 demonstrated lower image noise than STD-HIR/3.0, LD-HIR/3.0, and LD-HIR/0.5.
  • LD-DLR/0.5 showed equivalent CNR and higher edge-rise slope (ERS) compared to STD-HIR/3.0, with similar noise texture (NPS).
  • Subjective image quality scores, including diagnostic confidence, were significantly higher for LD-DLR/0.5 than for STD-HIR/3.0.

Conclusions:

  • Low-dose thin-slice DLR significantly improves image quality in pediatric abdominal CT.
  • DLR enhances edge sharpness, small structure visualization, and diagnostic confidence.
  • This technique offers a promising approach to reduce radiation dose while maintaining or improving diagnostic image quality in pediatric patients.
Abstract

Keywords:
Deep learningImage enhancementMultidetector computed tomographyPediatricsRadiation exposure

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