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Related Experiment Videos

Generative Adversarial Networks: A Primer for Radiologists.

Jelmer M Wolterink1, Anirban Mukhopadhyay1, Tim Leiner1

  • 1From the Department of Applied Mathematics, Faculty of Electrical Engineering, Mathematics and Computer Science, Technical Medical Centre, University of Twente, Zilverling, PO Box 217, 7500 AE Enschede, the Netherlands (J.M.W.); Department of Biomedical Engineering and Physics (J.M.W., I.I.) and Department of Radiology and Nuclear Medicine (I.I.), Amsterdam University Medical Center, Amsterdam, the Netherlands; Department of Informatics, Technische Universität Darmstadt, Darmstadt, Germany (A.M.); Department of Radiology, Utrecht University Medical Center, Utrecht, the Netherlands (T.L.); and Institute of Diagnostic and Interventional Radiology, Universitätsklinikum Frankfurt, Frankfurt, Germany (T.J.V., A.M.B.).

Radiographics : a Review Publication of the Radiological Society of North America, Inc
|April 23, 2021
PubMed
Summary

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Generative adversarial networks (GANs), a type of artificial intelligence, are revolutionizing radiology by synthesizing realistic medical images. These deep learning models enhance image quality, accelerate scans, and aid in abnormality detection.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Deep Learning

Background:

  • Artificial intelligence (AI), specifically deep learning, is poised to significantly impact radiology.
  • Generative Adversarial Networks (GANs) represent a key advancement in deep learning for medical imaging.

Purpose of the Study:

  • To introduce Generative Adversarial Networks (GANs) and adversarial deep learning methods.
  • To describe GAN applications in radiologic image synthesis and image-to-image translation.
  • To discuss the clinical potential, future applications, and limitations of GANs in radiology.

Main Methods:

  • Explanation of GANs comprising two competing neural networks: a generator and a discriminator.
  • Description of conditional GANs and cycle-consistent GANs principles.

Related Experiment Videos

  • Inclusion of illustrated examples of GAN applications across various imaging modalities and tasks.

    • Main Results:

    • GANs enable the synthesis of new, realistic medical images.
    • Applications include accelerating image acquisition and reducing artifacts.
    • GANs facilitate cross-modality image conversion and abnormality identification.

    Conclusions:

    • GANs offer diverse applications in radiologic image analysis, from synthesis to artifact reduction.
    • The review highlights the clinical potential and future directions for GANs in radiology.
    • Radiologists should be aware of the potential pitfalls and caveats associated with GAN implementation.