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dc.contributor.authorLorentzen, Rolf Johan
dc.contributor.authorNævdal, Geir
dc.contributor.authorSævareid, Ove
dc.contributor.authorHodneland, Erlend
dc.contributor.authorHanson, Erik Andreas
dc.contributor.authorMunthe-Kaas, Antonella Zanna
dc.date.accessioned2024-01-16T09:56:56Z
dc.date.available2024-01-16T09:56:56Z
dc.date.created2023-10-27T12:22:38Z
dc.date.issued2023
dc.identifier.issn1553-734X
dc.identifier.urihttps://hdl.handle.net/11250/3111730
dc.description.abstractThe measurement of perfusion and filtration of blood in biological tissue give rise to important clinical parameters used in diagnosis, follow-up, and therapy. In this paper, we address techniques for perfusion analysis using processed contrast agent concentration data from dynamic MRI acquisitions. A new methodology for analysis is evaluated and verified using synthetic data generated on a tissue geometry.en_US
dc.language.isoengen_US
dc.publisherPLoSen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titlePerfusion estimation using synthetic MRI-based measurements and a porous media flow modelen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.source.articlenumbere1011127en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.1371/journal.pcbi.1011127
dc.identifier.cristin2189175
dc.source.journalPLoS Computational Biologyen_US
dc.relation.projectNorges forskningsråd: 262203en_US
dc.identifier.citationPLoS Computational Biology. 2023, 19 (10), e1011127.en_US
dc.source.volume19en_US


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