Linked Life Data

21529085

Source:http://linkedlifedata.com/resource/pubmed/id/21529085

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2011-5-2
pubmed:abstractText
Generally, the performance of tomographic bioluminescence imaging is dependent on several factors, such as regularization parameters and initial guess of source distribution. In this paper, a global-inexact-Newton based reconstruction method, which is regularized by a dynamic sparse term, is presented for tomographic reconstruction. The proposed method can enhance higher imaging reliability and efficiency. In vivo mouse experimental reconstructions were performed to validate the proposed method. Reconstruction comparisons of the proposed method with other methods demonstrate the applicability on an entire region. Moreover, the reliable performance on a wide range of regularization parameters and initial unknown values were also investigated. Based on the in vivo experiment and a mouse atlas, the tolerance for optical property mismatch was evaluated with optical overestimation and underestimation. Additionally, the reconstruction efficiency was also investigated with different sizes of mouse grids. We showed that this method was reliable for tomographic bioluminescence imaging in practical mouse experimental applications.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1560-2281
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
046016
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models.
pubmed:affiliation
Chinese Academy of Sciences, Medical Image Processing Group, Institute of Automation, Beijing 100190, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't