Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.

ABSTRACT: Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new effici

SUBMITTER: Liu Y 

PROVIDER: S-EPMC4388626 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature