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Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method.

Hiroyuki UetaniTakeshi NakauraMika KitajimaKosuke MoritaKentaro HaraokaNaoki ShinojimaMachiko TateishiTaihei InoueAkira SasaoAkitake MukasaMinako AzumaOsamu IkedaYasuyuki YamashitaToshinori Hirai
Published in: European radiology (2022)
• The signal-to-noise ratios of cerebrospinal fluid progressively increased with the hybrid DLR method, with an increase in the denoising level for cerebrospinal fluid in pituitary T2WI with CS. • The signal-to-noise ratios of cerebrospinal fluid using the conventional wavelet method did not increase at higher denoising levels. • All qualitative scores of hybrid deep-learning reconstructions at all denoising levels were higher than those for the wavelet denoising method.
Keyphrases
  • convolutional neural network
  • deep learning
  • cerebrospinal fluid
  • magnetic resonance imaging
  • artificial intelligence
  • air pollution