Deep learning based multiplexed sensitivity-encoding (DL-MUSE) for high-resolution multi-shot DWI

•A deep learning-based phase reconstruction scheme is proposed for high-resolution multi-shot (MSH) DWI reconstruction.•Higher SNR (especially with high acceleration rates), fewer aliasing artifacts, lower ghost-to-signal-ratio (GSR), higher tracked fiber counts, and finer fiber delineation can be o...

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Vydané v:	NeuroImage (Orlando, Fla.) Ročník 244; s. 118632
Hlavní autori:	Zhang, Hui, Wang, Chengyan, Chen, Weibo, Wang, Fanwen, Yang, Zidong, Xu, Shuai, Wang, He
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 01.12.2021 Elsevier Limited Elsevier
Predmet:	Accuracy Adult Aged Algorithms Brain Neoplasms - diagnostic imaging Deep Learning Diffusion Diffusion Magnetic Resonance Imaging - methods Echo-Planar Imaging Efficiency Female High resolution Humans Male Middle Aged Multi-shot EPI Neural networks Neural Networks, Computer Neuroimaging Neuroimaging - methods Patients Phase correction Phase Variation Phase variations Substantia alba Tumors Young Adult High resolution GSR DL SSH-EPI Phase correction Multi-shot EPI deep learning SNR DWI DTI Diffusion MSH-EPI MUSE
ISSN:	1053-8119, 1095-9572, 1095-9572
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