SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

Journal: Magnetic resonance in medicine
Published Date: May 10, 2024

Abstract

PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation.

Authors

  • Yuyang Hu
    College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
  • Weijie Gan
    Department of Computer Science & Engineering.
  • Chunwei Ying
    Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.
  • Tongyao Wang
    Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri.
  • Cihat Eldeniz
    Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri.
  • Jiaming Liu
    Department of Electrical and Systems Engineering, University in St. Louis, St. Louis, MO, USA.
  • Yasheng Chen
    Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri.
  • Hongyu An
    Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri.
  • Ulugbek S Kamilov
    Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA.

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