Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.

Journal: Magnetic resonance in medicine
Published Date:

Abstract

PURPOSE: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA).

Authors

  • Mario O MalavĂ©
    Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
  • Corey A Baron
    Department of Medical Biophysics, Western University, London, ON, Canada.
  • Srivathsan P Koundinyan
    Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
  • Christopher M Sandino
    Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
  • Frank Ong
    Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
  • Joseph Y Cheng
  • Dwight G Nishimura
    Department of Electrical Engineering, Stanford University, Stanford, California.