Document Detail

Fast X-Ray CT Image Reconstruction Using a Linearized Augmented Lagrangian Method with Ordered Subsets.
MedLine Citation:
PMID:  25248178     Owner:  NLM     Status:  Publisher    
Augmented Lagrangian (AL) methods for solving convex optimization problems with linear constraints are attractive for imaging applications with composite cost functions due to the empirical fast convergence rate under weak conditions. However, for problems such as X-ray computed tomography (CT) image reconstruction, where the inner least-squares problem is challenging and requires iterations, AL methods can be slow. This paper focuses on solving regularized (weighted) least-squares problems using a linearized variant of AL methods that replaces the quadratic AL penalty term in the scaled augmented Lagrangian with its separable quadratic surrogate (SQS) function, leading to a simpler ordered-subsets (OS) accelerable splittingbased algorithm, OS-LALM. To further accelerate the proposed algorithm, we use a second-order recursive system analysis to design a deterministic downward continuation approach that avoids tedious parameter tuning and provides fast convergence. Experimental results show that the proposed algorithm significantly accelerates the convergence of X-ray CT image reconstruction with negligible overhead and can reduce OS artifacts when using many subsets.
Hung Nien; Jeffrey Fessler
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-9-16
Journal Detail:
Title:  IEEE transactions on medical imaging     Volume:  -     ISSN:  1558-254X     ISO Abbreviation:  IEEE Trans Med Imaging     Publication Date:  2014 Sep 
Date Detail:
Created Date:  2014-9-23     Completed Date:  -     Revised Date:  2014-9-24    
Medline Journal Info:
Nlm Unique ID:  8310780     Medline TA:  IEEE Trans Med Imaging     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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