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

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第一作者： Tomoko,Maruyama

第一单位： Division of Radiology, Shinshu University Hospital, Matsumoto, Nagano, 390-8621, Japan. tmaruyama@shinshu-u.ac.jp.;Department of Radiological Technology, Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, 371-0052, Japan. tmaruyama@shinshu-u.ac.jp.

作者： Tomoko,Maruyama ^[1] ; Norio,Hayashi ^[2] ; Yusuke,Sato ^[3] ; Toshihiro,Ogura ^[2] ; Masumi,Uehara ^[2] ; Haruyuki,Watanabe ^[2] ; Yoshihiro,Kitoh ^[4]

作者单位： Division of Radiology, Shinshu University Hospital, Matsumoto, Nagano, 390-8621, Japan. tmaruyama@shinshu-u.ac.jp.;Department of Radiological Technology, Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, 371-0052, Japan. tmaruyama@shinshu-u.ac.jp. ^[1] Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, 371-0052, Japan. ^[2] Department of Radiology, Gunma University Hospital, Maebashi, Gunma, 371-8511, Japan. ^[3] Division of Radiology, Shinshu University Hospital, Matsumoto, Nagano, 390-8621, Japan. ^[4]

关键词 Artifact removal Batch normalization layer Deep learning Dropout layer Image quality improvement MRI U-Net

医学主题词伪迹(Artifacts);磁共振成像(Magnetic Resonance Imaging);人类(Humans);图像处理, 计算机辅助(Image Processing, Computer-Assisted);运动(Motion)(Motion);脑(Brain);算法(Algorithms)

DOI 10.1186/s12880-025-01663-8

PMID 40312665

发布时间 2025-05-04

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准确一般字义偏差语意偏离译文作用小提交