Linked Life Data

21995068

Source:http://linkedlifedata.com/resource/pubmed/id/21995068

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2011-10-14
pubmed:abstractText
In vivo MicroCT imaging of disease models at multiple time points is of great importance for preclinical oncological research, to monitor disease progression. However, the great postural variability between animals in the imaging device complicates data comparison. In this paper we propose a method for automated registration of whole-body MicroCT follow-up datasets of mice. First, we register the skeleton, the lungs and the skin of an articulated animal atlas (Segars et al. 2004) to MicroCT datasets, yielding point correspondence of these structures over all time points. This correspondence is then used to regularize an intensity-based B-spline registration. This two step approach combines the robustness of model-based registration with the high accuracy of intensity-based registration. We demonstrate our approach using challenging whole-body in vivo follow-up MicroCT data and obtain subvoxel accuracy for the skeleton and the skin, based on the Euclidean surface distance. The method is computationally efficient and enables high resolution whole-body registration in approximately 17 minutes with unoptimized code, mostly executed single-threaded.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
516-23
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Automated registration of whole-body follow-up MicroCT data of mice.
pubmed:affiliation
Div. of Image Processing, Leiden University Medical Center, The Netherlands.
pubmed:publicationType
Journal Article