Frame-based compressive sensing MR image reconstruction with balanced regularization

Frame-based compressive sensing MR image reconstruction with balanced regularization
Title:
Frame-based compressive sensing MR image reconstruction with balanced regularization
Other Titles:
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publication Date:
25 August 2015
Citation:
S. Xie, C. Guan, W. Huang and Z. Lu, "Frame-based compressive sensing MR image reconstruction with balanced regularization," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 7031-7034. doi: 10.1109/EMBC.2015.7320011
Abstract:
This paper addresses the frame-based MR image reconstruction from undersampled k-space measurements by using a balanced ℓ1-regularized approach. Analysis-based and synthesis-based approaches are two common methods in ℓ1-regularized image restoration. They are equivalent under the orthogonal transform, but there exists a gap between them under redundant transform such as frame. Thus the third approach was developed to reduce the gap by penalizing the distance between the representation vector and the canonical frame coefficient of the estimated image, this balanced approach bridges the synthesis-based and analysis-based approaches and balances the fidelity, sparsity and smoothness of the solution. These frame-based approaches have been studied and compared for optical image restoration over the last few years. In this paper, we further study and compare these three approaches for the compressed sensing MR image reconstruction under redundant frame domain. These ℓ1-regularized optimization problems are solved by using a variable splitting strategy and the classical alternating direction method of multiplier (ADMM). Numerical simulation results show that the balanced approach can reduce the gap between the analysis-based and synthesis-based approaches and are even better than these two approaches under our experimental conditions.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1094-687X
1558-4615
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