Low-Rank and Sparse Matrix Decomposition Based on Schatten p= 1/2 and L1/2 Regularizations for Separation of Background and Dynamic Components for Dynamic MRI - Liang Yong

A method for determining a background component and a dynamic component of an image frame from an under-sampled data sequence obtained in a dynamic MRI application is provided. The two components are determined by optimizing a low-rank component and a sparse component of the image frame in a sense of minimizing a weighted sum of terms. The terms include a Schatten p= 1/2 (S 1/2-norm) of the low-rank component, an L 1/2-norm of the sparse component additionally sparsified by a sparsifying transform, and an L 2-norm of a difference between the sensed data sequence and a reconstructed data sequence. The reconstructed one is obtained by sub-sampling the image frame according to an encoding or acquiring operation. The background and dynamic components are the low-rank and sparse components, respectively. Experimental results demonstrate that the method outperforms an existing …