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dc.contributor.authorAndersen, Hilde Kjernlie
dc.contributor.authorVolgyes, David
dc.contributor.authorMartinsen, Anne Catrine Trægde
dc.date.accessioned2019-02-22T13:14:57Z
dc.date.available2019-02-22T13:14:57Z
dc.date.created2018-11-16T10:50:02Z
dc.date.issued2018
dc.identifier.citationEuropean Journal of Radiology Open. 2018, 5 35-40.nb_NO
dc.identifier.issn2352-0477
dc.identifier.urihttp://hdl.handle.net/11250/2587060
dc.description.abstractBackground Iterative reconstruction techniques for reducing radiation dose and improving image quality in CT have proved to work differently for different patient sizes, dose levels, and anatomical areas. Purpose This study aims to compare image quality in CT of the lungs between four high-end CT scanners using the recommended reconstruction techniques at different dose levels and patient sizes. Material and methods A lung phantom and an image quality phantom were scanned with four high-end scanners at fixed dose levels. Images were reconstructed with and without iterative reconstruction. Contrast-to-noise ratio, modulation transfer function, and peak frequency of the noise power spectrum were measured. Results IMR1 Sharp+ and VEO improved contrast-to-noise ratio to a larger extent than the other iterative techniques, while maintaining spatial resolution. IMR1 Sharp+ also maintained noise texture. Conclusions IMR1 Sharp+ was the only reconstruction technique in this study which increased CNR to a large extent, while maintaining all other image quality parameters measured in this study.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleImage quality with iterative reconstruction techniques in CT of the lungs?A phantom studynb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber35-40nb_NO
dc.source.volume5nb_NO
dc.source.journalEuropean Journal of Radiology Opennb_NO
dc.identifier.doi10.1016/j.ejro.2018.02.002
dc.identifier.cristin1631361
dc.description.localcode© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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