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) perfo...

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Hlavní autoři:	Seyed Amir Hossein Hosseini, Chi Zhang, Sebastian Weingärtner, Steen Moeller, Matthias Stuber, Kamil Ugurbil, Mehmet Akçakaya
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Public Library of Science (PLoS) 2020-01-01
Edice:	PLoS ONE
On-line přístup:	https://doi.org/10.1371/journal.pone.0229418
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