Manifold learning has achieved success in cardiac MRI. It models the dynamic images as points on a smooth, low dimensional manifold in high dimensional space. The low dimensional assumption is extracted as a regularizer, but corresponding algorithms are not performed along with the manifold's nonlinear structure. In this paper, we propose a deep manifold learning for dynamic MR imaging. The manifold assumption is no longer taken as the regularization term in the proposed method, but the deep optimization model is directly developed on the nonlinear manifold. The validation on in vivo data shows that our method can achieve improved reconstruction.
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