Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling

PURPOSE: To accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks. METHODS: Self-consistent robust artificial-neural-networks for k-space interpolation (sRAKI) per...

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Detalles Bibliográficos
Autores principales: Hosseini, Seyed Amir Hossein, Zhang, Chi, Weingärtner, Sebastian, Moeller, Steen, Stuber, Matthias, Ugurbil, Kamil, Akçakaya, Mehmet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034900/
https://www.ncbi.nlm.nih.gov/pubmed/32084235
http://dx.doi.org/10.1371/journal.pone.0229418

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034900/
https://www.ncbi.nlm.nih.gov/pubmed/32084235
http://dx.doi.org/10.1371/journal.pone.0229418

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