Low-Rank Tensor Subspace Decomposition With Weighted Group Sparsity for the Acceleration of Non-Cartesian Dynamic MRI

Jointly using group sparsity and sparsity can obtain better results than that using group sparsity alone, and weighted regularization can achieve better results than that without weighted regularization. The proposed algorithm results in reduced reconstruction error and improved image structural similarity in comparison with several state-of-the-art methods at relatively high acceleration factors. The proposed algorithm has the potential in various dynamic MRI application scenarios.