Highly Undersampled magnetic resonance image reconstruction using two-level Bregman method with dictionary updating

IEEE Transactions on Medical Imaging

Volumn 32, Issue 7, 2013, Pages 1290-1301

  Liu, Qiegen ^a   Wang, Shanshan ^c,d   Yang, Kun ^e   Luo, Jianhua ^c   Zhu, Yuemin ^f   Liang, Dong ^b  

^a NANCHANG UNIVERSITY   (China)

^b SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

^d UNIVERSITY OF SYDNEY   (Australia)

^e NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^f UNIVERSITÉ LYON 1   (France)

Author keywords

Augmented Lagrangian; Bregman iterative method; dictionary updating; image reconstruction; magnetic resonance imaging (MRI); sparse representation

Indexed keywords

AUGMENTED LAGRANGIAN METHODS; AUGMENTED LAGRANGIANS; BREGMAN ITERATIVE METHODS; LOCAL STRUCTURE; NUMBER OF ITERATIONS; OPTIMIZATION TECHNIQUES; OVERLAPPING IMAGES; SPARSE REPRESENTATION;

ALGORITHMS; IMAGE RECONSTRUCTION; INVERSE PROBLEMS; LAGRANGE MULTIPLIERS; MAGNETIC RESONANCE IMAGING;

ITERATIVE METHODS;

ANALYTIC METHOD; ARTICLE; BOOK; BREGMAN ITERATIVE METHOD; CODING; COMPUTER SIMULATION; CONVERGENT VALIDITY; DIAGNOSTIC VALUE; IMAGE RECONSTRUCTION; LEARNING ALGORITHM; NUCLEAR MAGNETIC RESONANCE IMAGING;

ALGORITHMS; BRAIN; CIRCLE OF WILLIS; COMPUTER SIMULATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LEAST-SQUARES ANALYSIS; LUMBAR VERTEBRAE; MAGNETIC RESONANCE IMAGING; PHANTOMS, IMAGING;

EID: 84880195831     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2013.2256464     Document Type: Article

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