Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics.

Journal: Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PMID: 32963184

Abstract

To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5.

Authors

  • Hajime Sagawa
    Division of Clinical Radiology Service, Kyoto University Hospital.
  • Yasutaka Fushimi
    Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University.
  • Satoshi Nakajima
    Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University.
  • Koji Fujimoto
    Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Kanae Kawai Miyake
    Department of Advanced Medical Imaging Research, Graduate School of Medicine, Kyoto University.
  • Hitomi Numamoto
    Department of Advanced Medical Imaging Research, Graduate School of Medicine, Kyoto University.
  • Koji Koizumi
    Division of Clinical Radiology Service, Kyoto University Hospital.
  • Masahito Nambu
    MRI Systems Division, Canon Medical Systems Corporation.
  • Hiroharu Kataoka
    Department of Neurosurgery, Graduate School of Medicine, Kyoto University.
  • Yuji Nakamoto
    Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University.
  • Tsuneo Saga
    Department of Advanced Medical Imaging Research, Graduate School of Medicine, Kyoto University.

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