Distortion-corrected image reconstruction with deep learning on an MRI-Linac.

Journal: Magnetic resonance in medicine
PMID: 37125656

Abstract

PURPOSE: MRI is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for MR-guided radiotherapy applications.

Authors

  • Shanshan Shan
    ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
  • Yang Gao
    State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100050, China.
  • Paul Z Y Liu
    ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
  • Brendan Whelan
    ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
  • Hongfu Sun
    School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Queensland, Australia.
  • Bin Dong
    Ricoh Software Research Center (Beijing), Beijing, China.
  • Feng Liu
    Department of Vascular and Endovascular Surgery, The First Medical Center of Chinese PLA General Hospital, 100853 Beijing, China.
  • David E J Waddington
    Image X Institute, University of Sydney, Sydney, Australia.

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