Repeatability-encouraging self-supervised learning reconstruction for quantitative MRI.

Journal: Magnetic resonance in medicine
Published Date: Feb 27, 2025

Abstract

PURPOSE: The clinical value of quantitative MRI hinges on its measurement repeatability. Deep learning methods to reconstruct undersampled quantitative MRI can accelerate reconstruction but do not aim to promote quantitative repeatability. This study proposes a repeatability-encouraging self-supervised learning (SSL) reconstruction method for quantitative MRI.

Authors

  • Zihao Chen
  • Zheyuan Hu
    National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore. Electronic address: e0792494@u.nus.edu.
  • Yibin Xie
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Debiao Li
  • Anthony G Christodoulou
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.

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