Document Detail


Convergent incremental optimization transfer algorithms: application to tomography.
MedLine Citation:
PMID:  16524085     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
No convergent ordered subsets (OS) type image reconstruction algorithms for transmission tomography have been proposed to date. In contrast, in emission tomography, there are two known families of convergent OS algorithms: methods that use relaxation parameters, and methods based on the incremental expectation-maximization (EM) approach. This paper generalizes the incremental EM approach by introducing a general framework, "incremental optimization transfer." The proposed algorithms accelerate convergence speeds and ensure global convergence without requiring relaxation parameters. The general optimization transfer framework allows the use of a very broad family of surrogate functions, enabling the development of new algorithms. This paper provides the first convergent OS-type algorithm for (nonconcave) penalized-likelihood (PL) transmission image reconstruction by using separable paraboloidal surrogates (SPS) which yield closed-form maximization steps. We found it is very effective to achieve fast convergence rates by starting with an OS algorithm with a large number of subsets and switching to the new "transmission incremental optimization transfer (TRIOT)" algorithm. Results show that TRIOT is faster in increasing the PL objective than nonincremental ordinary SPS and even OS-SPS yet is convergent.
Authors:
Sangtae Ahn; Jeffrey A Fessler; Doron Blatt; Alfred O Hero
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  IEEE transactions on medical imaging     Volume:  25     ISSN:  0278-0062     ISO Abbreviation:  IEEE Trans Med Imaging     Publication Date:  2006 Mar 
Date Detail:
Created Date:  2006-03-09     Completed Date:  2006-06-12     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  8310780     Medline TA:  IEEE Trans Med Imaging     Country:  United States    
Other Details:
Languages:  eng     Pagination:  283-96     Citation Subset:  IM    
Affiliation:
Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor 48109-2122, USA. sangtaea@usc.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Artificial Intelligence*
Humans
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods*
Information Storage and Retrieval / methods
Pattern Recognition, Automated / methods*
Positron-Emission Tomography / methods*
Grant Support
ID/Acronym/Agency:
CA-60711/CA/NCI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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