Linked Life Data

19694263

Source:http://linkedlifedata.com/resource/pubmed/id/19694263

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-8-21
pubmed:abstractText
We present a technique for automatic intensity-based image-to-physical registration of a 3-D segmentation for image-guided interventions. The registration aligns the segmentation with tracked and calibrated 3-D ultrasound (US) images of the target region. The technique uses a probabilistic framework and explicitly incorporates a model of the US image acquisition process. The rigid body registration parameters are varied to maximise the likelihood that the real US image(s) were formed using the US imaging model from the probe transducer position. The proposed technique is validated on images segmented from cardiac magnetic resonance imaging (MRI) data and 3-D US images acquired from 3 volunteers and 1 patient. We show that the accuracy of the algorithm is 2.6-4.2mm and the capture range is 9-18mm. The proposed technique has the potential to provide accurate image-to-physical registrations for a range of image guidance applications.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1011-2499
pubmed:author
pubmed:issnType
Print
pubmed:volume
21
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
188-201
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Image-to-physical registration for image-guided interventions using 3-D ultrasound and an ultrasound imaging model.
pubmed:affiliation
Division of Imaging Sciences, King's College London, UK. andrew.king@kcl.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't