Login / Signup

Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation.

Akifumi HagiwaraY OtsukaMasaaki HoriYasuhiko TachibanaKazumasa YokoyamaShohei FujitaChristina AndicaKoji KamagataRyusuke IrieSaori KoshinoTomoko MaekawaLydia ChougarAkihiko WadaM Y TakemuraNobutaka HattoriShigeki Aoki
Published in: AJNR. American journal of neuroradiology (2019)
Using deep learning, we improved the synthetic FLAIR image quality by generating FLAIR images that have contrast closer to that of conventional FLAIR images and fewer granular and swelling artifacts, while preserving the lesion contrast.
Keyphrases
  • deep learning
  • image quality
  • convolutional neural network
  • artificial intelligence
  • magnetic resonance
  • computed tomography
  • contrast enhanced
  • magnetic resonance imaging
  • dual energy