Uncertainty-aware physics-driven deep learning network for free-breathing liver fat and R * quantification using self-gated stack-of-radial MRI.

Journal: Magnetic resonance in medicine
Published Date: Apr 1, 2023

Abstract

PURPOSE: To develop a deep learning-based method for rapid liver proton-density fat fraction (PDFF) and R * quantification with built-in uncertainty estimation using self-gated free-breathing stack-of-radial MRI.

Authors

  • Shu-Fu Shih
    Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
  • Sevgi Gokce Kafali
    Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
  • Kara L Calkins
    Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
  • Holden H Wu
    Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.

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