Linked Life Data

8000842

Source:http://linkedlifedata.com/resource/pubmed/id/8000842

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
802
pubmed:dateCreated
1995-1-26
pubmed:abstractText
Image synthesis methods are based on the hypothesis that a magnetic resonance (MR) image with optimized contrast can be reproduced by synthesis from three calculated basic images of T1, T2 and spin density. This method, however, is limited by noise due to uncertainties in the initial measurements. The principal component analysis (PCA) method is based on an information theory approach that decomposes MR images into a small set of characteristic feature images. PCA images, or eigenimages, show morphology by condensing the structural information from the source images. Eigenimages have also been shown to improve contrast-to-noise ratio (CNR) compared with source images. In this study we have developed a method of synthesizing MR images using a flexible model, comprising a set of eigenimages derived from PCA. A matching process has been carried out to find the best fit between the model and a synthetic image calculated from the Bloch equations. The method has been applied to MR images obtained from a group of patients with intracranial lesions. The images derived from the flexible model show increased lesion conspicuity, reduced artefact and comparable CNR to the directly acquired images while maintaining the MR characteristic information for diagnosis.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
AIM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0007-1285
pubmed:author
pubmed:issnType
Print
pubmed:volume
67
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
976-82
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1994
pubmed:articleTitle
Magnetic resonance image synthesis using a flexible model.
pubmed:affiliation
Department of Diagnostic Radiology, University of Manchester, UK.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't