Manifold Constrained Low-rank and Joint Sparse Learning for Dynamic Cardiac MRI

Qingmin Meng, Xianchao Xiu, Yan Li
2020 IEEE Access  
Reconstruction from highly accelerated dynamic magnetic resonance imaging (MRI) is of great significance for medical diagnosis. The application of low-rank and sparse matrix decomposition to MRI can improve imaging speed and efficiency. However, the consistence of the learned low-rank and sparse structures for similar input samples is not well addressed in literature. In this paper, we propose a manifold constrained low-rank and joint sparse learning model that embeds the manifold priors into
more » ... wrank and joint sparse decomposition. It is noted that the joint sparsity is investigated to exploit the shared information. Further, the manifold constraints for low-rank and joint sparse parts are forced the optimization process to satisfy the structure preservation requirement. To solve the above manifold learning problem, a manifold constrained alternating direction method of multipliers (McADMM) approach is designed. It is proved theoretically that the sequence generated by McADMM converges to a stationary point. Numerical comparisons on simulation data and real-world dynamic cardiac MRI data are presented to demonstrate its efficiency.
doi:10.1109/access.2020.3014236 fatcat:s2jy3xqpsrhnda46oswkk3b5hu