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 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.

Methods

This study developed a U-Net-based regression network for the removal of motion artifacts and investigated the impact of combining BN and dropout layers as a strategy for this pu

SUBMITTER: Maruyama T 

PROVIDER: S-EPMC12046729 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature