Linked Life Data

19904028

Source:http://linkedlifedata.com/resource/pubmed/id/19904028

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
23
pubmed:dateCreated
2009-11-20
pubmed:abstractText
We developed a maximum a posterior (MAP) reconstruction method for positron emission tomography (PET) image reconstruction incorporating magnetic resonance (MR) image information, with the joint entropy between the PET and MR image features serving as the regularization constraint. A non-parametric method was used to estimate the joint probability density of the PET and MR images. Using realistically simulated PET and MR human brain phantoms, the quantitative performance of the proposed algorithm was investigated. Incorporation of the anatomic information via this technique, after parameter optimization, was seen to dramatically improve the noise versus bias tradeoff in every region of interest, compared to the result from using conventional MAP reconstruction. In particular, hot lesions in the FDG PET image, which had no anatomical correspondence in the MR image, also had improved contrast versus noise tradeoff.Corrections were made to figures 3, 4 and 6, and to the second paragraph of section 3.1 on 13 November 2009. The corrected electronic version is identical to the print version.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1361-6560
pubmed:author
pubmed:issnType
Electronic
pubmed:day
7
pubmed:volume
54
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
7063-75
pubmed:dateRevised
2011-7-28
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Bayesian PET image reconstruction incorporating anato-functional joint entropy.
pubmed:affiliation
Department of Radiology, The Johns Hopkins University, Baltimore, MD 21287, USA. jtang18@jhmi.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't