Document Detail

An algorithm for sparse MRI reconstruction by Schatten p-norm minimization.
MedLine Citation:
PMID:  20952139     Owner:  NLM     Status:  In-Data-Review    
In recent years, there has been a concerted effort to reduce the MR scan time. Signal processing research aims at reducing the scan time by acquiring less K-space data. The image is reconstructed from the subsampled K-space data by employing compressed sensing (CS)-based reconstruction techniques. In this article, we propose an alternative approach to CS-based reconstruction. The proposed approach exploits the rank deficiency of the MR images to reconstruct the image. This requires minimizing the rank of the image matrix subject to data constraints, which is unfortunately a nondeterministic polynomial time (NP) hard problem. Therefore we propose to replace the NP hard rank minimization problem by its nonconvex surrogate - Schatten p-norm minimization. The same approach can be used for denoising MR images as well. Since there is no algorithm to solve the Schatten p-norm minimization problem, we derive an efficient first-order algorithm. Experiments on MR brain scans show that the reconstruction and denoising accuracy from our method is at par with that of CS-based methods. Our proposed method is considerably faster than CS-based methods.
Angshul Majumdar; Rabab K Ward
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Publication Detail:
Type:  Journal Article     Date:  2010-10-16
Journal Detail:
Title:  Magnetic resonance imaging     Volume:  29     ISSN:  1873-5894     ISO Abbreviation:  Magn Reson Imaging     Publication Date:  2011 Apr 
Date Detail:
Created Date:  2011-03-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8214883     Medline TA:  Magn Reson Imaging     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  408-17     Citation Subset:  IM    
Copyright Information:
Copyright © 2011 Elsevier Inc. All rights reserved.
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