Impact of deep learning architectures on accelerated cardiac T1 mapping using MyoMapNet

The objective of the current study was to investigate the performance of various deep learning (DL) architectures for MyoMapNet, a DL model for T1 estimation using accelerated cardiac T1 mapping from four T1‐weighted images collected after a single inversion pulse (Look‐Locker 4 [LL4]). We implement...

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Veröffentlicht in:	NMR in biomedicine Jg. 35; H. 11; S. e4794 - n/a
Hauptverfasser:	Amyar, Amine, Guo, Rui, Cai, Xiaoying, Assana, Salah, Chow, Kelvin, Rodriguez, Jennifer, Yankama, Tuyen, Cirillo, Julia, Pierce, Patrick, Goddu, Beth, Ngo, Long, Nezafat, Reza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Wiley Subscription Services, Inc 01.11.2022
Schlagworte:	Accuracy Artificial neural networks Biological products cardiac MRI Coders Data collection Deep learning Heart Image quality Inversion inversion‐recovery cardiac T1 mapping Mapping myocardial tissue characterization Myocardium Neural networks Performance evaluation Training
ISSN:	0952-3480, 1099-1492, 1099-1492
Online-Zugang:	Volltext
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