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Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms.

Luuk J OostveenFrederick J A MeijerFrank de LangeEwoud J SmitSjoert A PeggeStefan C A SteensMartin J van AmerongenMathias ProkopIoannis Sechopoulos
Published in: European radiology (2021)
• Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.
Keyphrases
  • image quality
  • deep learning
  • dual energy
  • computed tomography
  • magnetic resonance
  • contrast enhanced
  • machine learning
  • subarachnoid hemorrhage
  • convolutional neural network
  • cerebral ischemia
  • photodynamic therapy