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

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

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 ti...

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Bibliographic Details
Main Authors: Tomoko Maruyama, Norio Hayashi, Yusuke Sato, Toshihiro Ogura, Masumi Uehara, Haruyuki Watanabe, Yoshihiro Kitoh
Format: Article
Language:English
Published: BMC 2025-05-01
Series:BMC Medical Imaging
Subjects:
MRI
Artifact removal
Deep learning
U-Net
Batch normalization layer
Dropout layer
Online Access:https://doi.org/10.1186/s12880-025-01663-8
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https://doi.org/10.1186/s12880-025-01663-8

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