Linked Life Data

21761697

Source:http://linkedlifedata.com/resource/pubmed/id/21761697

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2011-7-18
pubmed:abstractText
We formulate registration-based elastography in a probabilistic framework and apply it to study lung elasticity in the presence of emphysematous and fibrotic tissue. The elasticity calculations are based on a Finite Element discretization of a linear elastic biomechanical model. We marginalize over the boundary conditions (deformation) of the biomechanical model to determine the posterior distribution over elasticity parameters. Image similarity is included in the likelihood, an elastic prior is included to constrain the boundary conditions, while a Markov model is used to spatially smooth the inhomogeneous elasticity. We use a Markov Chain Monte Carlo (MCMC) technique to characterize the posterior distribution over elasticity from which we extract the most probable elasticity as well as the uncertainty of this estimate. Even though registration-based lung elastography with inhomogeneous elasticity is challenging due the problem's highly underdetermined nature and the sparse image information available in lung CT, we show promising preliminary results on estimating lung elasticity contrast in the presence of emphysematous and fibrotic tissue.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1011-2499
pubmed:author
pubmed:issnType
Print
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
699-710
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Probabilistic elastography: estimating lung elasticity.
pubmed:affiliation
Surgical Planning Lab, Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts USA. pettri@bwh.harvard.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural