Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components.

ABSTRACT:

Purpose

To apply the low-rank plus sparse (L+S) matrix decomposition model to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest.

Theory and methods

The L+S model is natural to represent dynamic MRI data. Incoherence between k-t space (acquisition) and the singular vectors of L and the sparse domain of S is required to reconstruct undersampled data. Incoherence between L and S is required for robust separation of background and dynamic components. Multicoil L+S reconstruction is formulated using a convex optimization approach, where the nuclear norm is used to enforce low rank in L and the l1 norm is used to enforce sparsity in S. Feasibility of the L+S reconstruction was tested in several dyn

SUBMITTER: Otazo R 

PROVIDER: S-EPMC4207853 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature