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Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction

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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 14220))

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Abstract

CT images corrupted by metal artifacts have serious negative effects on clinical diagnosis. Considering the difficulty of collecting paired data with ground truth in clinical settings, unsupervised methods for metal artifact reduction are of high interest. However, it is difficult for previous unsupervised methods to retain structural information from CT images while handling the non-local characteristics of metal artifacts. To address these challenges, we proposed a novel D ense T ransformer based E nhanced C oding Net work (DTEC-Net) for unsupervised metal artifact reduction. Specifically, we introduce a Hierarchical Disentangling Encoder, supported by the high-order dense process, and transformer to obtain densely encoded sequences with long-range correspondence. Then, we present a second-order disentanglement method to improve the dense sequence’s decoding process. Extensive experiments and model discussions illustrate DTEC-Net’s effectiveness, which outperforms the previous state-of-the-art methods on a benchmark dataset, and greatly reduces metal artifacts while restoring richer texture details.

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Notes

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Acknowledgements

This research work was undertaken in the context of Horizon 2020 MSCA ETN project “xCTing” (Project ID: 956172).

Author information

Authors and Affiliations

  1. Center for Processing Speech and Images, Department of ESAT, KU Leuven, Leuven, Belgium

    Wangduo Xie & Matthew B. Blaschko

Authors
  1. Wangduo Xie
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  2. Matthew B. Blaschko
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Corresponding author

Correspondence to Wangduo Xie .

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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Xie, W., Blaschko, M.B. (2023). Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-43907-0_8

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  • Online ISBN: 978-3-031-43907-0

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