Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network.

Journal: AJNR. American journal of neuroradiology
Published Date: Feb 13, 2020

Abstract

BACKGROUND AND PURPOSE: Motion artifacts are a frequent source of image degradation in the clinical application of MR imaging (MRI). Here we implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural network.

Authors

  • K Sommer
    From Philips Research, (K.S., A.S., T.B.) Hamburg, Germany karsten.sommer@philips.com.
  • A Saalbach
    From Philips Research, (K.S., A.S., T.B.) Hamburg, Germany.
  • T Brosch
    From Philips Research, (K.S., A.S., T.B.) Hamburg, Germany.
  • C Hall
    Radiology Solutions (C.H.), Philips, Seattle, Washington.
  • N M Cross
    Department of Radiology (N.M.C., J.B.A.), University of Washington, Seattle, Washington.
  • J B Andre
    Department of Radiology (N.M.C., J.B.A.), University of Washington, Seattle, Washington.

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