Training a neural network for Gibbs and noise removal in diffusion MRI.

Journal: Magnetic resonance in medicine
Published Date:

Abstract

PURPOSE: To develop and evaluate a neural network-based method for Gibbs artifact and noise removal.

Authors

  • Matthew J Muckley
    Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.
  • Benjamin Ades-Aron
    Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.
  • Antonios Papaioannou
    Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.
  • Gregory Lemberskiy
    Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.
  • Eddy Solomon
    Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.
  • Yvonne W Lui
    Center for Advanced Imaging Innovation and Research (CAI2R), School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA; Bernard and Irene Schwartz Center for Biomedical Imaging, School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA.
  • Daniel K Sodickson
    Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA.
  • Els Fieremans
  • Dmitry S Novikov
    Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.
  • Florian Knoll
    Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA.