Artifact estimation network for MR images: effectiveness of batch normalization and dropout layers.

Journal: BMC medical imaging
PMID: 40312665

Abstract

BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject to limitations including extended scan times. Deep learning architectures, such as U-Net, may be able to address these limitations. Optimizing deep learning networks with batch normalization (BN) and dropout layers enhances their convergence and accuracy. However, the influence of this strategy on U-Net has not been explored for artifact removal.

Authors

  • Tomoko Maruyama
    Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
  • Norio Hayashi
    Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
  • Yusuke Sato
    Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
  • Toshihiro Ogura
    Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
  • Masumi Uehara
    Department of Radiological Technology, Gunma Prefectural College of Health Sciences, 323-1 Kamioki, Maebashi, Gunma, 371-0052, Japan.
  • Haruyuki Watanabe
    Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.
  • Yoshihiro Kitoh
    Division of Radiology, Shinshu University Hospital, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan.

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