Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.

Journal: Magnetic resonance in medicine
Published Date: Jul 10, 2023

Abstract

PURPOSE: To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans.

Authors

  • Arjun D Desai
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Batu M Ozturkler
    Department of Electrical Engineering, Stanford University, Stanford, California, USA.
  • Christopher M Sandino
    Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
  • Robert Boutin
    Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
  • Marc Willis
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Shreyas Vasanawala
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Brian A Hargreaves
    Department of Radiology, Stanford University, Stanford, California.
  • Christopher Ré
    1Stanford University, Stanford, CA USA.
  • John M Pauly
    Department of Electrical Engineering, Stanford University, Stanford, California, USA.
  • Akshay S Chaudhari
    Department of Radiology, Stanford University, Stanford, California.

Keywords

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