Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning

The aim of this study was to develop a deep neural network for respiratory motion compensation in free‐breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteers and patients who underwent clinically indicated...

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Vydané v:	NMR in biomedicine Ročník 34; číslo 2; s. e4433 - n/a
Hlavní autori:	Ghodrati, Vahid, Bydder, Mark, Ali, Fadil, Gao, Chang, Prosper, Ashley, Nguyen, Kim‐Lien, Hu, Peng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Wiley Subscription Services, Inc 01.02.2021
Predmet:	adversarial autoencoder, cardiovascular magnetic resonance, deep learning, magnetic resonance imaging, respiratory motion correction Artificial neural networks Biological products Breath Holding Breathing Coders Computer Simulation Feasibility studies Heart Heart - diagnostic imaging Humans Image Processing, Computer-Assisted - methods Image quality In vivo methods and tests Machine learning Magnetic resonance imaging Magnetic Resonance Imaging, Cine - methods Medical imaging Motion Motion compensation Neural networks Neural Networks, Computer Performance evaluation Respiration Simulation Statistics, Nonparametric Unsupervised learning Unsupervised Machine Learning adversarial autoencoder, cardiovascular magnetic resonance, deep learning, magnetic resonance imaging, respiratory motion correction
ISSN:	0952-3480, 1099-1492, 1099-1492
On-line prístup:	Získať plný text
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