Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

@article{Yang2018LowDoseCI,
  title={Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss},
  author={Qingsong Yang and Pingkun Yan and Yanbo Zhang and Hengyong Yu and Yongyi Shi and Xuanqin Mou and Mannudeep K. Kalra and Yi Zhang and Ling Sun and Ge Wang},
  journal={IEEE Transactions on Medical Imaging},
  year={2018},
  volume={37},
  pages={1348-1357}
}
The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists’ judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past… CONTINUE READING

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