A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI.

Journal: Magnetic resonance in medicine
Published Date: Apr 29, 2021

Abstract

PURPOSE: With rising safety concerns over the use of gadolinium-based contrast agents (GBCAs) in contrast-enhanced MRI, there is a need for dose reduction while maintaining diagnostic capability. This work proposes comprehensive technical solutions for a deep learning (DL) model that predicts contrast-enhanced images of the brain with approximately 10% of the standard dose, across different sites and scanners.

Authors

  • Srivathsa Pasumarthi
    Subtle Medical Inc., Menlo Park, CA, USA.
  • Jonathan I Tamir
    Subtle Medical Inc., Menlo Park, CA, USA.
  • Soren Christensen
    1 Stanford Stroke Center, Stanford University Medical Center, Stanford, CA, USA.
  • Greg Zaharchuk
    Stanford University, Stanford CA 94305, USA.
  • Tao Zhang
    Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.
  • Enhao Gong
    Department of Electrical Engineering, Stanford University, Stanford, California, USA.

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