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Generative Adversarial Network Powered Fast Magnetic Resonance Imaging—Comparative Study and New Perspectives

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Generative Adversarial Learning: Architectures and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 217))

Abstract

Magnetic Resonance Imaging (MRI) is a vital component of medical imaging. When compared to other image modalities, it has advantages such as the absence of radiation, superior soft tissue contrast, and complementary multiple sequence information. However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical. Traditional compressive sensing based MRI (CS-MRI) reconstruction can speed up MRI acquisition, but suffers from a long iterative process and noise-induced artefacts. Recently, Deep Neural Networks (DNNs) have been used in sparse MRI reconstruction models to recreate relatively high-quality images from heavily undersampled k-space data, allowing for much faster MRI scanning. However, there are still some hurdles to tackle. For example, directly training DNNs based on L1/L2 distance to the target fully sampled images could result in blurry reconstruction because L1/L2 loss can only enforce overall image or patch similarity and does not take into account local information such as anatomical sharpness. It is also hard to preserve fine image details while maintaining a natural appearance. More recently, Generative Adversarial Networks (GAN) based methods are proposed to solve fast MRI with enhanced image perceptual quality. The encoder obtains a latent space for the undersampling image, and the image is reconstructed by the decoder using the GAN loss. In this chapter, we review the GAN powered fast MRI methods with a comparative study on various anatomical datasets to demonstrate the generalisability and robustness of this kind of fast MRI while providing future perspectives.

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Acknowledgements

This study was supported in part by the British Heart Foundation [Project Number: TG/18/5/34111, PG/16/78/32402], the European Research Council Innovative Medicines Initiative [DRAGON, H2020-JTI-IMI2 101005122], the AI for Health Imaging Award [CHAIMELEON, H2020-SC1-FA-DTS-2019-1 952172], and the UK Research and Innovation Future Leaders Fellowship [MR/V023799/1].

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Authors and Affiliations

  1. National Heart & Lung Institute, Imperial College London, London, SW7 2AZ, UK

    Guang Yang & Yutong Chen

  2. Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK

    Guang Yang

  3. School of Computer and Control Engineering, Yantai University, Yantai, China

    Jun Lv

  4. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, CB2 3EG, UK

    Yutong Chen

  5. School of Optics and Photonics, Beijing Institute of Technology, Beijing, China

    Jiahao Huang

  6. Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK

    Jin Zhu

Authors
  1. Guang Yang
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  2. Jun Lv
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  3. Yutong Chen
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  4. Jiahao Huang
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  5. Jin Zhu
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Corresponding author

Correspondence to Guang Yang .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering and School of Computer Science, University of Windsor, Windsor, ON, Canada

    Roozbeh Razavi-Far

  2. SeeChange.ai, Manchester, UK

    Ariel Ruiz-Garcia

  3. Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, Switzerland

    Vasile Palade

  4. The Swiss AI Lab, IDSIA, University of Lugano, USI & SUPSI, Lugano, Switzerland

    Juergen Schmidhuber

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Yang, G., Lv, J., Chen, Y., Huang, J., Zhu, J. (2022). Generative Adversarial Network Powered Fast Magnetic Resonance Imaging—Comparative Study and New Perspectives. In: Razavi-Far, R., Ruiz-Garcia, A., Palade, V., Schmidhuber, J. (eds) Generative Adversarial Learning: Architectures and Applications. Intelligent Systems Reference Library, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-030-91390-8_13

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