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Dynamic MR Image Reconstruction From Highly Undersampled (k, t)-Space Data Exploiting Low Tensor Train Rank and Sparse Prior
Dynamic magnetic resonance imaging (dynamic MRI) is used to visualize living tissues and their changes over time. In this paper, we propose a new tensor-based dynamic MRI approach for reconstruction from highly undersampled (k, t)-space data, which combines low tensor train rankness and temporal sparsity constraints. Considering tensor train (TT) …