MOLLI

Improving accuracy of myocardial T1 estimation in MyoMapNet
To improve the accuracy and robustness of T1 estimation by MyoMapNet, a deep learning–based approach using 4 inversion‐recovery T1‐weighted images for cardiac T1 mapping.
Rui Guo, Zhensen Chen, Amine Amyar, Hossam El‐Rewaidy, Salah Assana
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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 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.
Amine Amyar, Rui Guo, Xiaoying Cai, Salah Assana
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Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach
To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4).
Rui Guo, Hossam El-Rewaidy, Salah Assana
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