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Solving Low-Dose CT Reconstruction via GAN with Local Coherence

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14229))

Abstract

The Computed Tomography (CT) for diagnosis of lesions in human internal organs is one of the most fundamental topics in medical imaging. Low-dose CT, which offers reduced radiation exposure, is preferred over standard-dose CT, and therefore its reconstruction approaches have been extensively studied. However, current low-dose CT reconstruction techniques mainly rely on model-based methods or deep-learning-based techniques, which often ignore the coherence and smoothness for sequential CT slices. To address this issue, we propose a novel approach using generative adversarial networks (GANs) with enhanced local coherence. The proposed method can capture the local coherence of adjacent images by optical flow, which yields significant improvements in the precision and stability of the constructed images. We evaluate our proposed method on real datasets and the experimental results suggest that it can outperform existing state-of-the-art reconstruction approaches significantly.

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Notes

  1. 1.

    If \(i=1\), \(\mathcal {N}(\mathbf {s_i})=\{\mathbf {s_{2}}\}\); if \(i=n\), \(\mathcal {N}(\mathbf {s_i})=\{\mathbf {s_{n-1}}\}\).

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Acknowledgements

The research of this work was supported in part by National Key R &D program of China through grant 2021YFA1000900, the NSFC throught Grant 62272432, and the Provincial NSF of Anhui through grant 2208085MF163.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China

    Wenjie Liu & Hu Ding

Authors
  1. Wenjie Liu
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  2. Hu Ding
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Corresponding author

Correspondence to Hu Ding .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen's University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Liu, W., Ding, H. (2023). Solving Low-Dose CT Reconstruction via GAN with Local Coherence. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_50

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  • DOI: https://doi.org/10.1007/978-3-031-43999-5_50

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