Abstract #0449

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

Hossam El-Rewaidy^1,2, 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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