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Reject rate analysis in digital radiography: an Australian emergency imaging department case study.

Samantha AtkinsonMichael J NeepDeborah Starkey
Published in: Journal of medical radiation sciences (2019)
The variation in radiographer reject rates and the high reject rate for some projections indicate that reject analysis is still necessary as a quality assurance tool for DR. A feedback system between radiologists and radiographers may reduce the high percentage of positioning errors by standardising the technical factors used to assess image quality. Future reject analysis should be conducted regularly incorporating an exposure indicator analysis as well as retrospective assessment of individual rejected images.
Keyphrases
  • image quality
  • healthcare
  • computed tomography
  • deep learning
  • machine learning
  • magnetic resonance imaging
  • magnetic resonance
  • cross sectional
  • mass spectrometry
  • patient safety
  • adverse drug