dc.contributor.author | Lorentzen, Rolf Johan | |
dc.contributor.author | Nævdal, Geir | |
dc.contributor.author | Sævareid, Ove | |
dc.contributor.author | Hodneland, Erlend | |
dc.contributor.author | Hanson, Erik Andreas | |
dc.contributor.author | Munthe-Kaas, Antonella Zanna | |
dc.date.accessioned | 2024-01-16T09:56:56Z | |
dc.date.available | 2024-01-16T09:56:56Z | |
dc.date.created | 2023-10-27T12:22:38Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 1553-734X | |
dc.identifier.uri | https://hdl.handle.net/11250/3111730 | |
dc.description.abstract | The 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.iso | eng | en_US |
dc.publisher | PLoS | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Perfusion estimation using synthetic MRI-based measurements and a porous media flow model | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright 2023 The Author(s) | en_US |
dc.source.articlenumber | e1011127 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |
dc.identifier.doi | 10.1371/journal.pcbi.1011127 | |
dc.identifier.cristin | 2189175 | |
dc.source.journal | PLoS Computational Biology | en_US |
dc.relation.project | Norges forskningsråd: 262203 | en_US |
dc.identifier.citation | PLoS Computational Biology. 2023, 19 (10), e1011127. | en_US |
dc.source.volume | 19 | en_US |