Linked Life Data

20426178

Source:http://linkedlifedata.com/resource/pubmed/id/20426178

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2010-4-29
pubmed:abstractText
In this paper we first discuss the technical challenges preventing an automated analysis of cardiac perfusion MR images and subsequently present a fully unsupervised workflow to address the problems. The proposed solution consists of key-frame detection, consecutive motion compensation, surface coil inhomogeneity correction using proton density images and robust generation of pixel-wise perfusion parameter maps. The entire processing chain has been implemented on clinical MR systems to achieve unsupervised inline analysis of perfusion MRI. Validation results are reported for 260 perfusion time series, demonstrating feasibility of the approach.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
12
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
741-9
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Unsupervised inline analysis of cardiac perfusion MRI.
pubmed:affiliation
Imaging and Visualization, Siemens Corporate Research, Princeton, NJ, USA. hui-xue@siemens.com
pubmed:publicationType
Journal Article