De-Aliasing and Accelerated Sparse Magnetic Resonance Image Reconstruction Using Fully Dense CNN with Attention Gates

De-Aliasing and Accelerated Sparse Magnetic Resonance Image Reconstruction Using Fully Dense CNN with Attention Gates

When sparsely sampled data are used to accelerate magnetic resonance imaging (MRI), conventional reconstruction approaches produce significant artifacts that obscure the content of the image. To remove aliasing artifacts, we propose an advanced convolutional neural network (CNN) called fully dense a...

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Bibliographic Details
Main Authors: Md. Biddut Hossain, Ki-Chul Kwon, Shariar Md Imtiaz, Oh-Seung Nam, Seok-Hee Jeon, Nam Kim
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Bioengineering
Subjects:
aliasing artifacts
attention gate
deep learning
fully dense network
MRI reconstruction
Online Access:https://www.mdpi.com/2306-5354/10/1/22

Internet

https://www.mdpi.com/2306-5354/10/1/22

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