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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Detalles Bibliográficos
Autores principales: Hossain, Md. Biddut, Kwon, Ki-Chul, Imtiaz, Shariar Md, Nam, Oh-Seung, Jeon, Seok-Hee, Kim, Nam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854709/
https://www.ncbi.nlm.nih.gov/pubmed/36671594
http://dx.doi.org/10.3390/bioengineering10010022

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854709/
https://www.ncbi.nlm.nih.gov/pubmed/36671594
http://dx.doi.org/10.3390/bioengineering10010022

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