Linked Life Data

9050403

Source:http://linkedlifedata.com/resource/pubmed/id/9050403

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
1997-3-24
pubmed:abstractText
A major drawback of statistical iterative image reconstruction for emission computed tomography is its high computational cost. The ill-posed nature of tomography leads to slow convergence for standard gradient-based iterative approaches such as the steepest descent or the conjugate gradient algorithm. In this paper new theory and methods for a class of preconditioners are developed for accelerating the convergence rate of iterative reconstruction. To demonstrate the potential of this class of preconditioners, a preconditioned conjugate gradient (PCG) iterative algorithm for weighted least squares reconstruction (WLS) was formulated for emission tomography. Using simulated positron emission tomography (PET) data of the Hoffman brain phantom, it was shown that the convergence rate of the PCG can reduce the number of iterations of the standard conjugate gradient algorithm by a factor of 2-8 times depending on the convergence criterion.
pubmed:grant
pubmed:language
eng
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:author
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1-10
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:articleTitle
A general class of preconditioners for statistical iterative reconstruction of emission computed tomography.
pubmed:affiliation
Department of Molecular and Medical Pharmacology, UCLA School of Medicine 90095-6948, USA. gchinn@poisson.nuc.ucla.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S.