<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Simon DiMaio</style></author><author><style face="normal" font="default" size="100%">Daniel Kacher</style></author><author><style face="normal" font="default" size="100%">Randy Ellis</style></author><author><style face="normal" font="default" size="100%">Gabor Fichtinger</style></author><author><style face="normal" font="default" size="100%">Nobuhiko Hata</style></author><author><style face="normal" font="default" size="100%">Gary Zientara</style></author><author><style face="normal" font="default" size="100%">Lawrence Panych</style></author><author><style face="normal" font="default" size="100%">Ron Kikinis</style></author><author><style face="normal" font="default" size="100%">Ferenc Jolesz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Needle artifact localization in 3T MR images</style></title><secondary-title><style face="normal" font="default" size="100%">Studies in health technology and informatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artifacts</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnetic Resonance Imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Needles</style></keyword><keyword><style  face="normal" font="default" size="100%">United States</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">http://perk.cs.queensu.ca/sites/perk.cs.queensu.ca/files/DiMaio2006.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Brigham, Women's Hospital, Harvard Medical School, Boston 02115, USA simond@bwh harvard edu</style></publisher><volume><style face="normal" font="default" size="100%">119</style></volume><pages><style face="normal" font="default" size="100%">120–125</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work explores an image-based approach for localizing needles during MRI-guided interventions, for the purpose of tracking, navigation Susceptibility artifacts for several needles of varying thickness were imaged, in phantoms, using a 3 tesla MRI system, under a variety of conditions The relationship between the true needle positions, the locations of artifacts within the images, determined both by manual, automatic segmentation methods, have been quantified, are presented here</style></abstract></record></records></xml>