Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.

Journal: AJNR. American journal of neuroradiology
PMID: 39947682

Abstract

BACKGOUND AND PURPOSE: This study utilizes a physics-based approach to synthesize realistic MR artifacts and train a deep learning generative adversarial network (GAN) for use in artifact reduction on EPI, a crucial neuroimaging sequence with high acceleration that is notoriously susceptible to artifacts.

Authors

  • Catalina Raymond
    UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA.
  • Jingwen Yao
    From the UCLA Brain Tumor Imaging Laboratory (C.R., J.Y., S.O., B.M.E.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA.
  • Bryan Clifford
    Siemens Medical Solutions USA, Boston, Massachusetts, USA.
  • Thorsten Feiweier
    Siemens Healthcare GmbH, Erlangen, Germany.
  • Sonoko Oshima
    Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
  • Donatello Telesca
  • Xiaodong Zhong
    Department of Radiological Sciences (C.R., J.Y., S.O., X.Z., N.S., B.M.E), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA.
  • Heiko Meyer
    Siemens Healthcare, Application Development, Erlangen, Germany.
  • Richard G Everson
    Department of Neurosurgery (R.G.E.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA.
  • Noriko Salamon
    Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA.
  • Timothy F Cloughesy
    UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA.
  • Benjamin M Ellingson
    UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.

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