.png){kind=icon}
Return
Bregman Iteration Based Efficient Algorithm for MR Image Reconstruction From Undersampled K-Space Data
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2013.2268206.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
It is difficult to solve Magnetic Resonance (MR) image reconstruction problems with linear combinations of total variation and l(1) norm regularization terms. In order to solve these compound regularization problems, we propose an efficient algorithm in this letter. The proposed algorithm adopts the Bregman iteration technique to convert the original constrained problem to a sequence of unconstrained problems, which are then solved using operator splitting and variable splitting techniques. Simulation experiments demonstrate that significant improvement of the quality of reconstructed images is achieved by the proposed algorithm when compared to previous methods.
Keywords:
Bregman iteration
compressed sensing
convex optimization
MR image reconstruction
operator splitting
total variation
variable splitting
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSave
ShareSaveA Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data
PREAI
ShareSave
View more