Reconstruction of Compressed-sensing MR Imaging Using Deep Residual Learning in the Image Domain

PURPOSE: A deep residual learning convolutional neural network (DRL-CNN) was applied to improve image quality and speed up the reconstruction of compressed sensing magnetic resonance imaging. The reconstruction performances of the proposed method was compared with iterative reconstruction methods. M...

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Detalles Bibliográficos
Autores principales: Ouchi, Shohei, Ito, Satoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Japanese Society for Magnetic Resonance in Medicine 2020
Materias:
Major Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203484/
https://www.ncbi.nlm.nih.gov/pubmed/32611937
http://dx.doi.org/10.2463/mrms.mp.2019-0139

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203484/
https://www.ncbi.nlm.nih.gov/pubmed/32611937
http://dx.doi.org/10.2463/mrms.mp.2019-0139

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