Dynamic MRI reconstruction exploiting partial separability and t-SVD

In this paper, we proposed a new method to reconstruct dynamic magnetic imaging (dMRI) data from highly undersampled k-t space measurements. First, we use the partial separability (PS) model to capture the spatiotemporal correlations of dMRI data. Then, we introduce a new tensor decomposition method named as tensor singular value decomposition (t-SVD) to the reconstruction problem. PS and low tensor multi-rank constrains are jointly enforced to reconstruct dynamic MRI data. We develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the proposed optimization problem. The experimental results demonstrate the superior performance of the proposed method.