Parametric cerebral blood flow and arterial transit time mapping using a 3D convolutional neural network

Purpose To reduce the total scan time of multiple postlabeling delay (multi‐PLD) pseudo‐continuous arterial spin labeling (pCASL) by developing a hierarchically structured 3D convolutional neural network (H‐CNN) that estimates the arterial transit time (ATT) and cerebral blow flow (CBF) maps from th...

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Veröffentlicht in:	Magnetic resonance in medicine Jg. 90; H. 2; S. 583 - 595
Hauptverfasser:	Kim, Donghoon, Lipford, Megan E., He, Hongjian, Ding, Qiuping, Ivanovic, Vladimir, Lockhart, Samuel N., Craft, Suzanne, Whitlow, Christopher T., Jung, Youngkyoo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 01.08.2023
Schlagworte:	arterial spin labeling arterial transit time Arteries Artificial neural networks Blood flow Cerebral blood flow Cerebrovascular Circulation - physiology convolutional neural network deep learning Female Flow mapping Humans Machine learning Magnetic Resonance Imaging - methods Male Mapping Neural networks Neural Networks, Computer Reproducibility of Results Spin labeling Spin Labels Transit time deep learning cerebral blood flow convolutional neural network arterial spin labeling arterial transit time
ISSN:	0740-3194, 1522-2594, 1522-2594
Online-Zugang:	Volltext
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