Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.

Journal: Magma (New York, N.Y.)
Published Date: May 14, 2024

Abstract

OBJECT: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability.

Authors

  • Chi Zhang
    Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Davide Piccini
    Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Omer Burak Demirel
  • Gabriele Bonanno
    Advanced Clinical Imaging Technology, Siemens Healthineers International, Lausanne, Switzerland.
  • Christopher W Roy
    Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Burhaneddin Yaman
  • Steen Moeller
    Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota.
  • Chetan Shenoy
    Department of Medicine (Cardiology), University of Minnesota, Minneapolis, Minnesota, USA.
  • Matthias Stuber
    Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
  • Mehmet Akçakaya
    Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota.

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