Linked Life Data

21772082

Source:http://linkedlifedata.com/resource/pubmed/id/21772082

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
15
pubmed:dateCreated
2011-7-20
pubmed:abstractText
Spatial smoothing using isotropic Gaussian kernels to remove noise reduces spatial resolution and increases the partial volume effect of functional magnetic resonance images (fMRI), thereby reducing localization power. To minimize these limitations, we propose a novel anisotropic smoothing method for fMRI data. To extract an anisotropic tensor for each voxel of the functional data, we derived an intensity gradient using the distance transformation of the segmented gray matter of the fMRI-coregistered T1-weighted image. The intensity gradient was then used to determine the anisotropic smoothing kernel at each voxel of the fMRI data. Performance evaluations on both real and simulated data showed that the proposed method had 10% higher statistical power and about 20% higher gray matter localization compared to isotropic smoothing and robustness to the registration errors (up to 4 mm translations and 4° rotations) between T1 structural images and fMRI data. The proposed method also showed higher performance than the anisotropic smoothing with diffusion gradients derived from the fMRI intensity data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1361-6560
pubmed:author
pubmed:issnType
Electronic
pubmed:day
7
pubmed:volume
56
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5063-77
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
A method for anisotropic spatial smoothing of functional magnetic resonance images using distance transformation of a structural image.
pubmed:affiliation
Department of Radiology, Nuclear Medicine and Severance Biomedical Science Institute, Brain Korea.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't