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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	NMR in biomedicine Ročník 35; číslo 11; s. e4794 - n/a
Hlavní autori:	Amyar, Amine, Guo, Rui, Cai, Xiaoying, Assana, Salah, Chow, Kelvin, Rodriguez, Jennifer, Yankama, Tuyen, Cirillo, Julia, Pierce, Patrick, Goddu, Beth, Ngo, Long, Nezafat, Reza
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Wiley Subscription Services, Inc 01.11.2022
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!