Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
Published Date: Sep 2, 2024

Abstract

BACKGROUND: Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, and prognosis assessment; however, the DLR on multi-modal glioma imaging has not been assessed.

Authors

  • Jun Sun
    School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu Province, PR China.
  • Siyao Xu
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China.
  • Yiding Guo
    Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China.
  • Jinli Ding
    Reproductive Medical Center, Renmin Hospital of Wuhan University and Hubei Clinic Research Center for Assisted Reproductive Technology and Embryonic Development, Wuhan 430060, China.
  • Zhizheng Zhuo
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Dabiao Zhou
    Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China.
  • Yaou Liu
    Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, PR China; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, PR China.

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