A deep error correction network for compressed sensing MRI

BACKGROUND: CS-MRI (compressed sensing for magnetic resonance imaging) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. Due to imperfect modelings in the inverse imaging, state-of-the-art CS-MRI methods tend to leave structural reconstruction errors....

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Detalles Bibliográficos
Autores principales: Sun, Liyan, Wu, Yawen, Fan, Zhiwen, Ding, Xinghao, Huang, Yue, Paisley, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7422575/
https://www.ncbi.nlm.nih.gov/pubmed/32903379
http://dx.doi.org/10.1186/s42490-020-0037-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7422575/
https://www.ncbi.nlm.nih.gov/pubmed/32903379
http://dx.doi.org/10.1186/s42490-020-0037-5

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