Conditional generative adversarial network for 3D rigid-body motion correction in MRI.

Journal: Magnetic resonance in medicine
Published Date: Apr 22, 2019

Abstract

PURPOSE: Subject motion in MRI remains an unsolved problem; motion during image acquisition may cause blurring and artifacts that severely degrade image quality. In this work, we approach motion correction as an image-to-image translation problem, which refers to the approach of training a deep neural network to predict an image in 1 domain from an image in another domain. Specifically, the purpose of this work was to develop and train a conditional generative adversarial network to predict artifact-free brain images from motion-corrupted data.

Authors

  • Patricia M Johnson
    Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada.
  • Maria Drangova
    Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.

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