Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors

Purpose Although recent deep learning methodologies have shown promising results in fast MR imaging, how to explore it to learn an explicit prior and leverage it into the observation constraint is still desired. Methods A denoising autoencoder (DAE) network is leveraged as an explicit prior to addre...

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Vydané v:	Magnetic resonance in medicine Ročník 83; číslo 1; s. 322 - 336
Hlavní autori:	Liu, Qiegen, Yang, Qingxin, Cheng, Huitao, Wang, Shanshan, Zhang, Minghui, Liang, Dong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Wiley Subscription Services, Inc 01.01.2020
Predmet:	Acceleration Algorithms autoencoding priors Brain - diagnostic imaging Computer Systems Humans Image processing Image Processing, Computer-Assisted - methods Image reconstruction Image restoration Machine learning Magnetic Resonance Imaging multichannel prior Neural Networks, Computer Noise generation Noise reduction proximal gradient descent Sampling Signal-To-Noise Ratio Software Trajectories multichannel prior autoencoding priors magnetic resonance imaging image reconstruction proximal gradient descent
ISSN:	0740-3194, 1522-2594, 1522-2594
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