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Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images.

Ganglin LiangJinpeng ZhouZixiang ChenLiwen WanXieraili WumenerYarong ZhangDong LiangYing LiangZhanli Hu
Published in: EJNMMI physics (2023)
The proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.
Keyphrases
  • deep learning
  • convolutional neural network
  • artificial intelligence
  • pet ct
  • positron emission tomography
  • computed tomography
  • machine learning
  • pet imaging
  • quality improvement
  • optical coherence tomography