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 Ying^1,2

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here