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Abstract #4782

Improving Accuracy of Myocardial T1 Estimation in MyoMapNet

Rui Guo1, Zhensen Chen2, Amine Amyar1, Hossam El-Reiwady1, Patrick Pierce1, Beth Goddu1, and Reza Nezafat1
1Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States, 2Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China


MOLLI T1 mapping is routinely used for myocardial tissue characterization. Despite excellent precision, MOLLI has great T1 underestimation and a long breath-holding time. We previously developed a deep learning-based approach (MyoMapNet) for myocardial T1 mapping using four T1-weighted images. MOLLI T1 was used to train a fully connected neural network (FC), which resulted in a similar accuracy error as MOLLI. In this study, numerically simulated and phantom signals with the presence of different B0, flip angles, and heart rates were used to improve MyoMapNet T1 accuracy. Evaluation showed that MyoMapNet T1 could be improved and had higher precision than SASHA.

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