• Corpus ID: 219177277

Dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network

@article{Lyu2020DualenergyCI,
  title={Dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network},
  author={Tianling Lyu and Zhan Wu and Yikun Zhang and Yang Chen and Lei Xing and Wei Zhao},
  journal={arXiv: Medical Physics},
  year={2020}
}
Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT… 

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