Partial separability (PS) and sparsity have been previously used to enable reconstruction of
dynamic images from undersampled (k, t)-space data. This paper presents a new method to
use PS and sparsity constraints jointly for enhanced performance in this context. The
proposed method combines the complementary advantages of PS and sparsity constraints
using a unified formulation, achieving significantly better reconstruction performance than
using either of these constraints individually. A globally convergent computational algorithm …

Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously
been demonstrated useful for image reconstruction from undersampled data. This paper
extends our early work in this area by proposing a new method for jointly enforcing the PS
and spatial total variation (TV) constraints for dynamic MR image reconstruction. An
algorithm is also described to solve the underlying optimization problem efficiently. The
proposed method has been validated using simulated cardiac imaging data, with the …