Modeling the MRI gradient system with a temporal convolutional network: Improved reconstruction by prediction of readout gradient errors.

Journal: Magnetic resonance in medicine
Published Date: Aug 18, 2025

Abstract

PURPOSE: Our objective is to develop a general, nonlinear gradient system model that can accurately predict gradient distortions using convolutional networks.

Authors

  • Jonathan B Martin
    Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Hannah E Alderson
    Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • John C Gore
    Vanderbilt University Institute of Imaging Science, USA. Electronic address: john.gore@vanderbilt.edu.
  • Mark D Does
    Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Kevin D Harkins
    Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America.

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