Improved dynamic MRI reconstruction by exploiting sparsity and rank-deficiency.

Magnetic resonance imaging

PubMedID: 23218793

Majumdar A. Improved dynamic MRI reconstruction by exploiting sparsity and rank-deficiency. Magn Reson Imaging. 2013;31(5):789-95.
In this paper we address the problem of dynamic MRI reconstruction from partially sampled K-space data. Our work is motivated by previous studies in this area that proposed exploiting the spatiotemporal correlation of the dynamic MRI sequence by posing the reconstruction problem as a least squares minimization regularized by sparsity and low-rank penalties. Ideally the sparsity and low-rank penalties should be represented by the l(0)-norm and the rank of a matrix; however both are NP hard penalties. The previous studies used the convex l(1)-norm as a surrogate for the l(0)-norm and the non-convex Schatten-q norm (0