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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Magnetic resonance in medicine Ročník 83; číslo 1; s. 322 - 336
Hlavní autoři:	Liu, Qiegen, Yang, Qingxin, Cheng, Huitao, Wang, Shanshan, Zhang, Minghui, Liang, Dong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Wiley Subscription Services, Inc 01.01.2020
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!