Linked Life Data

16685927

Source:http://linkedlifedata.com/resource/pubmed/id/16685927

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2006-5-11
pubmed:abstractText
The paper describes a method for automatic detection of colonic polyps, robust enough to be directly applied to low-dose CT colonographic datasets. Polyps are modeled using gray level intensity profiles and extended Gaussian images. Spherical harmonic decompositions ensure an easy comparison between a polyp candidate and a set of polypoid models, found in a previously built database. The detection sensitivity and specificity values are evaluated at different dose levels. Starting from the original raw-data (acquired at 55mAs), 5 patient datasets (prone and supine scans) are reconstructed at different dose levels (down to 5mAs), using different kernel filters and slice increments. Although the image quality decreases when lowering the acquisition mAs, all polyps above 6mm are successfully detected even at 15 mAs. Accordingly the effective dose can be reduced from 4.93mSv to 1.61 mSv, without affecting detection capabilities, particularly important when thinking of population screening.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
859-67
pubmed:dateRevised
2009-12-11
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Computer aided detection for low-dose CT colonography.
pubmed:affiliation
Faculties of Medicine & Engineering, Medical Image Computing (Radiology - ESAT/PSI), University Hospital Gasthuisberg, Herestraat 49, B3000 Leuven, Belgium.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies