Adaptive fixed-point iterative shrinkage/thresholding algorithm for MR imaging reconstruction using compressed sensing

Recently compressed sensing (CS) has been applied to under-sampling MR image reconstruction for significantly reducing signal acquisition time. To guarantee the accuracy and efficiency of the CS-based MR image reconstruction, it necessitates determining several regularization and algorithm-introduce...

Full description

Saved in:
Bibliographic Details
Published in:Magnetic resonance imaging Vol. 32; no. 4; pp. 372 - 378
Main Authors: Wu, Geming, Luo, Shuqian
Format: Journal Article
Language:English
Published: Netherlands Elsevier Inc 01.05.2014
Subjects:
Algorithms
Brain - anatomy & histology
Compressed sensing
Data Compression - methods
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Non-linear reconstruction
Numerical Analysis, Computer-Assisted
Radiology
Regularization parameter
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Non-linear reconstruction
Magnetic resonance imaging
Regularization parameter
Compressed sensing
ISSN:0730-725X, 1873-5894, 1873-5894
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently compressed sensing (CS) has been applied to under-sampling MR image reconstruction for significantly reducing signal acquisition time. To guarantee the accuracy and efficiency of the CS-based MR image reconstruction, it necessitates determining several regularization and algorithm-introduced parameters properly in practical implementations. The regularization parameter is used to control the trade-off between the sparsity of MR image and the fidelity measures of k-space data, and thus has an important effect on the reconstructed image quality. The algorithm-introduced parameters determine the global convergence rate of the algorithm itself. These parameters make CS-based MR image reconstruction a more difficult scheme than traditional Fourier-based method while implemented on a clinical MR scanner. In this paper, we propose a new approach that reveals that the regularization parameter can be taken as a threshold in a fixed-point iterative shrinkage/thresholding algorithm (FPIST) and chosen by employing minimax threshold selection method. No extra parameter is introduced by FPIST. The simulation results on synthetic and real complex-valued MRI data show that the proposed method can adaptively choose the regularization parameter and effectively achieve high reconstruction quality. The proposed method should prove very useful for practical CS-based MRI applications.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:0730-725X
1873-5894
1873-5894
DOI:10.1016/j.mri.2013.12.009