Title
Evaluation of maximum-likelihood based attenuation correction in positron emission tomography Evaluation of maximum-likelihood based attenuation correction in positron emission tomography
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
article
Publication
New York, N.Y. ,
Subject
Human medicine
Source (journal)
IEEE transactions on nuclear science. - New York, N.Y.
Volume/pages
46(1999) :4 , p. 1136-1141
ISSN
0018-9499
ISI
000082566000026
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
In positron emission tomography, transmission scans are used to correct the acquired data for the effect of photon attenuation. The noise present in the transmission measurement often has a significant effect on the signal to noise ratio of the final attenuation corrected image of the tracer distribution. This study evaluates the effect of different attenuation correction strategies on the performance of human observers in a tumor detection task. The four strategies considered are: no attenuation correction, multiplication with the count ratio of blank and transmission sinograms, reprojection of a maximum-likelihood reconstruction, and reprojection of a maximum-a-posteriori reconstruction of the transmission sinogram. Performance in tumor detection was quantified as the contrast at which the number of errors increased beyond 20%. No statistically significant difference was found between classical attenuation correction and maximum-likelihood based correction. With maximum-a-posteriori based attenuation correction performance was significantly better than with the other methods
E-info
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000082566000026&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000082566000026&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000082566000026&DestLinkType=CitingArticles&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848