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Abstract #0387

Deep-Learning-Based Motion Correction For Quantitative Cardiac MRI

Alfredo De Goyeneche1, Shuyu Tang1, Nii Okai Addy1, Bob Hu1, William Overall1, and Juan Santos1
1HeartVista, Inc., Los Altos, CA, United States

We developed a deep-learning-based approach for motion correction in quantitative cardiac MRI, including perfusion, T1 mapping, and T2 mapping. The proposed approach consists of a segmentation network and a registration network. The segmentation network was trained using 2D short-axis images for each of the three sequences, while the same registration network was shared between all three sequences. The proposed approach was faster and more accurate than a popular traditional registration method. Our work is beneficial for building a faster and more robust automated processing pipeline to obtain CMR parametric maps.

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