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Related Concept Videos

X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
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Published on: September 11, 2011

AI-Based Post-processing for Artefact Mitigation in Radiography: A Systematic Review.

Stanley A Norris1,2, Harry Marland3, Darcy Stephenson4

  • 1Monash Radiology, Monash Health, 246 Clayton Rd, Clayton, Melbourne, VIC, 3168, Australia. stan.norris@monash.edu.

Journal of Imaging Informatics in Medicine
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) methods show promise for reducing radiography artefacts. However, current research lacks prospective studies and detailed reporting, hindering clinical use of these AI-based image quality enhancement techniques.

Keywords:
ArtefactsArtificial IntelligenceCLAIMImage processingProjectional radiography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Radiography Artefact Mitigation

Background:

  • Projectional radiography is prone to artefacts that degrade image quality and complicate interpretation.
  • Artificial intelligence (AI) offers potential solutions for post-processing artefact removal in radiographic images.

Purpose of the Study:

  • To systematically review AI-based post-processing methods for artefact mitigation in radiography.
  • To identify challenges hindering clinical translation of these AI techniques.
  • To evaluate reporting quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

Main Methods:

  • Systematic literature search of multiple databases (pre-09/01/2026) for AI post-processing in radiography artefact removal.
  • Inclusion of original peer-reviewed articles, excluding pre-reconstruction data methods and hardware-based approaches.
  • Independent screening by two reviewers, with CLAIM assessment for reporting quality.

Main Results:

  • 10 studies (2020-2025) identified from 2965 records, focusing on bone shadow, device, and general artefact removal.
  • Generative Adversarial Networks (GANs) were the dominant AI architecture.
  • No prospective studies were found; methodological reporting was insufficient for reproducibility, with limited CLAIM adherence (mean score 17.4/42).

Conclusions:

  • The current evidence base for AI-based radiography artefact mitigation is insufficient for clinical adoption.
  • Future research requires multi-institutional testing, prospective designs, and transparent reporting (open data/code).
  • Focus on clinically meaningful endpoints and failure mode analysis is crucial for advancing AI in medical imaging.