Linked Life Data

18666100

Source:http://linkedlifedata.com/resource/pubmed/id/18666100

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2008-8-4
pubmed:abstractText
SENSE reconstruction suffers from an ill-conditioning problem, which increasingly lowers the signal-to-noise ratio (SNR) as the reduction factor increases. Ill-conditioning also degrades the convergence behavior of iterative conjugate gradient reconstructions for arbitrary trajectories. Regularization techniques are often used to alleviate the ill-conditioning problem. Based on maximum a posteriori statistical estimation with a Huber Markov random field prior, this study presents a new method for adaptive regularization using the image and noise statistics. The adaptive Huber regularization addresses the blurry edges in Tikhonov regularization and the blocky effects in total variation (TV) regularization. Phantom and in vivo experiments demonstrate improved image quality and convergence speed over both the unregularized conjugate gradient method and Tikhonov regularization method, at no increase in total computation time.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1522-2594
pubmed:author
pubmed:copyrightInfo
(c) 2008 Wiley-Liss, Inc.
pubmed:issnType
Electronic
pubmed:volume
60
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
414-21
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
A statistical approach to SENSE regularization with arbitrary k-space trajectories.
pubmed:affiliation
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53211, USA. leiying@uwm.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't, Evaluation Studies