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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 244; S. 118632
Hauptverfasser:	Zhang, Hui, Wang, Chengyan, Chen, Weibo, Wang, Fanwen, Yang, Zidong, Xu, Shuai, Wang, He
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.12.2021 Elsevier Limited Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!