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Abstract #0441

MLS: Self-learned joint manifold geometry and sparsity aware framework for highly accelerated cardiac cine imaging

Ukash Nakarmi1, Konstantinos Slavakis1, Hongyu Li1, Chaoyi Zhang1, Peizhou Huang1, Sunil Gaire1, and Leslie Ying1,2

1Electrical Engineering, University at Buffalo, Buffalo, NY, United States, 2Biomedical Engineering, University at Buffalo, Buffalo, NY, United States

In this work, we propose a novel joint manifold learning and sparsity aware framework for highly accelerated cardiac cine imaging. The proposed method efficiently captures the intrinsic low dimensional nonlinear manifold geometry and inherent periodicity of cardiac data, and outperforms the current state-of-the-art accelerated MRI methods.

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