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Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

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Abstract

Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.

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Notes

  1. https://www.kaggle.com/c/data-science-bowl-2017/data

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Acknowledgements

The authors would like to thank Troy Anderson for the acquisition of the piglet dataset.

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  1. University of Saskatchewan, College of Medicine, Saskatoon, SK, Canada

    Xin Yi & Paul Babyn

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Yi, X., Babyn, P. Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network. J Digit Imaging 31, 655–669 (2018). https://doi.org/10.1007/s10278-018-0056-0

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