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

• Published in: NMR in biomedicine Vol. 35; no. 11; pp. e4794 - n/a
• Main Authors: 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
• Language: English
• Published: Oxford Wiley Subscription Services, Inc 01.11.2022
• Subjects: 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

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...