In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of
MR image sequences that are highly undersampled in k-space. By formulating the
reconstruction as a high-order low-rank plus sparse tensor decomposition problem, we
propose an efficient numerical algorithm based on the alternating direction method of
multipliers (ADMM) to solve the optimization. Through extensive experimental results we
show that our proposed method achieves superior reconstruction quality, compared to the …

The low-rank plus sparse (L+ S) decomposition model has enabled better reconstruction of
dynamic magnetic resonance imaging (dMRI) with separation into background (L) and
dynamic (S) component. However, use of low-rank prior alone may not fully explain the slow
variations or smoothness of the background part at the local scale. In this paper, we propose
a smoothness-regularized L+ S (SR-L+ S) model for dMRI reconstruction from highly
undersampled kt-space data. We exploit joint low-rank and smooth priors on the background …