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Accelerated MRI Reconstruction with Dual-Domain Generative Adversarial Network

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Machine Learning for Medical Image Reconstruction (MLMIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11905))

Abstract

Fast reconstruction of under-sampled acquisitions has always been a central issue in MRI reconstruction. Recently years has seen multiple studies using deep learning as a de-aliasing framework to restore the aliased image. However, restoration of fine details is still problematic, especially when dealing with noisy image datasets. Sparked by the Fourier transform relationship, this work proposed and tested a new hypothesis: can regularization be directly added in the frequency domain to correct the high-frequency imperfection? To achieve this, discriminative networks are applied in both the image domain and the frequency domain (so-called dual-domain GAN). Evaluation on multiple datasets proved that the dual-domain GAN approach is an effective way to improve the quality of accelerated MR reconstruction.

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Author information

Authors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Guanhua Wang

  2. Subtle Medical, Inc., Menlo Park, CA, USA

    Enhao Gong

  3. Stanford University, Stanford, CA, USA

    Enhao Gong, John Pauly & Greg Zaharchuk

  4. GE Healthcare, Chicago, IL, USA

    Suchandrima Banerjee

Authors
  1. Guanhua Wang
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  2. Enhao Gong
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  3. Suchandrima Banerjee
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  4. John Pauly
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  5. Greg Zaharchuk
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Corresponding author

Correspondence to Greg Zaharchuk .

Editor information

Editors and Affiliations

  1. New York University, New York, NY, USA

    Florian Knoll

  2. University of Erlangen-Nuremberg, Erlangen, Germany

    Andreas Maier

  3. Imperial College London, London, UK

    Daniel Rueckert

  4. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jong Chul Ye

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Wang, G., Gong, E., Banerjee, S., Pauly, J., Zaharchuk, G. (2019). Accelerated MRI Reconstruction with Dual-Domain Generative Adversarial Network. In: Knoll, F., Maier, A., Rueckert, D., Ye, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2019. Lecture Notes in Computer Science(), vol 11905. Springer, Cham. https://doi.org/10.1007/978-3-030-33843-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-33843-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33842-8

  • Online ISBN: 978-3-030-33843-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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