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Abstract #0449

MyoMapNet: A Deep Neural Network for Accelerating the Modified Look-Locker Inversion Recovery Myocardial T1 Mapping to 5 Heart Beats

Hossam El-Rewaidy1,2, Rui Guo1, and Reza Nezafat1
1Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States, 2Graduate School of Bioengineering, Department of Computer Science, Technical University of Munich, Munich, Germany

In this work, we developed and evaluated a rapid (4-5 heartbeats) myocardial T1 mapping approach by estimating voxel-wise T1 values from one look-locker (LL) experiment of MOLLI sequence using a fully-connected neural network (MyoMapNet). MyoMapNet consists of 5 hidden layers that map the input 4-5 T1-weighted samplings and their inversion times into T1 values. MyoMapNet was trained and evaluated on a large dataset of native MOLLI-5(3)3 T1 in 717 subjects and post-contrast MOLLI-4(1)3(1)2 in 535 subjects. MyoMapNet showed similar T1 estimations to MOLLI-5(3)3 and MOLLI-4(1)3(1)2 T1 (mean difference=1±17ms, and -3±18ms, respectively, p-value >0.1 for both).

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