Transformer-based deep learning denoising of single and multi-delay 3D arterial spin labeling.

Journal: Magnetic resonance in medicine
PMID: 37849048

Abstract

PURPOSE: To present a Swin Transformer-based deep learning (DL) model (SwinIR) for denoising single-delay and multi-delay 3D arterial spin labeling (ASL) and compare its performance with convolutional neural network (CNN) and other Transformer-based methods.

Authors

  • Qinyang Shou
    From the Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles (K.W., Q.S., S.J.M., H.K., D.J.J.W.).
  • Chenyang Zhao
    SILC Business School, Shanghai University, Shanghai 201800, China.
  • Xingfeng Shao
    Laboratory of Functional MRI Technology (LOFT), Stevens Neuro Imaging and Informatics Institute, University of Southern California, Los Angeles, California, USA.
  • Kay Jann
    Laboratory of Functional MRI Technology (LOFT), Stevens Neuro Imaging and Informatics Institute, University of Southern California, Los Angeles, California, USA.
  • Hosung Kim
    Laboratory of Neuro Imaging, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA. Electronic address: hosung.kim@loni.usc.edu.
  • Karl G Helmer
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
  • Hanzhang Lu
    The Russell H. Morgan, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Danny J J Wang
    From the Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles (K.W., Q.S., S.J.M., H.K., D.J.J.W.).

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