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Multislice input for 2D and 3D residual convolutional neural network noise reduction in CT.

Zhongxing ZhouNathan R HuberAkitoshi InoueCynthia H McColloughLifeng Yu
Published in: Journal of medical imaging (Bellingham, Wash.) (2023)
We conclude the that multislice input is an effective strategy for improving performance for 2D deep CNN denoising models. The pure 3D CNN model tends to have a better performance than the other models in terms of continuity across axial slices, but the difference was not significant compared with the 2D CNN model with the same number of slices as the input.
Keyphrases
  • convolutional neural network
  • deep learning
  • computed tomography
  • air pollution
  • magnetic resonance imaging
  • magnetic resonance
  • dual energy