Linked Life Data

15876673

Source:http://linkedlifedata.com/resource/pubmed/id/15876673

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2005-5-6
pubmed:abstractText
We present a regularized Gauss-Newton method for solving the inverse problem of parameter reconstruction from boundary data in frequency-domain diffuse optical tomography. To avoid the explicit formation and inversion of the Hessian which is often prohibitively expensive in terms of memory resources and runtime for large-scale problems, we propose to solve the normal equation at each Newton step by means of an iterative Krylov method, which accesses the Hessian only in the form of matrix-vector products. This allows us to represent the Hessian implicitly by the Jacobian and regularization term. Further we introduce transformation strategies for data and parameter space to improve the reconstruction performance. We present simultaneous reconstructions of absorption and scattering distributions using this method for a simulated test case and experimental phantom data.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0031-9155
pubmed:author
pubmed:issnType
Print
pubmed:day
21
pubmed:volume
50
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2365-86
pubmed:dateRevised
2007-8-13
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Gauss-Newton method for image reconstruction in diffuse optical tomography.
pubmed:affiliation
Department of Computer Science, University College London, Gower Street London WC1E 6BT, UK.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies