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Frequency-Supervised MR-to-CT Image Synthesis

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 13003))

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Abstract

This paper strives to generate a synthetic computed tomography (CT) image from a magnetic resonance (MR) image. The synthetic CT image is valuable for radiotherapy planning when only an MR image is available. Recent approaches have made large strides in solving this challenging synthesis problem with convolutional neural networks that learn a mapping from MR inputs to CT outputs. In this paper, we find that all existing approaches share a common limitation: reconstruction breaks down in and around the high-frequency parts of CT images. To address this common limitation, we introduce frequency-supervised deep networks to explicitly enhance high-frequency MR-to-CT image reconstruction. We propose a frequency decomposition layer that learns to decompose predicted CT outputs into low- and high-frequency components, and we introduce a refinement module to improve high-frequency reconstruction through high-frequency adversarial learning. Experimental results on a new dataset with 45 pairs of 3D MR-CT brain images show the effectiveness and potential of the proposed approach. Code is available athttps://github.com/shizenglin/Frequency-Supervised-MR-to-CT-Image-Synthesis.

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Author information

Authors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Zenglin Shi, Pascal Mettes & Cees Snoek

  2. Shanghai Jiao Tong University, Shanghai, China

    Guoyan Zheng

Authors
  1. Zenglin Shi
  2. Pascal Mettes
  3. Guoyan Zheng
  4. Cees Snoek

Corresponding author

Correspondence toZenglin Shi.

Editor information

Editors and Affiliations

  1. Universitätsklinikum Heidelberg, Heidelberg, Germany

    Sandy Engelhardt

  2. Istanbul Technical University, Istanbul, Turkey

    Ilkay Oksuz

  3. The University of Texas at Arlington, Arlington, TX, USA

    Dajiang Zhu

  4. University of Hong Kong, Hong Kong, Hong Kong

    Yixuan Yuan

  5. TU Darmstadt, Darmstadt, Germany

    Anirban Mukhopadhyay

  6. University of Minnesota, Minneapolis, MN, USA

    Nicholas Heller

  7. Pennsylvania State University, University Park, PA, USA

    Sharon Xiaolei Huang

  8. University of Houston, Houston, TX, USA

    Hien Nguyen

  9. University of Bern, Bern, Switzerland

    Raphael Sznitman

  10. Johns Hopkins University, Baltimore, MD, USA

    Yuan Xue

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Shi, Z., Mettes, P., Zheng, G., Snoek, C. (2021). Frequency-Supervised MR-to-CT Image Synthesis. In: Engelhardt, S.,et al. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. DGM4MICCAI DALI 2021 2021. Lecture Notes in Computer Science(), vol 13003. Springer, Cham. https://doi.org/10.1007/978-3-030-88210-5_1

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