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dc.contributor.authorJurek, Jakub
dc.contributor.authorReisæter, Lars Anders Rokne
dc.contributor.authorKocinski, Marek
dc.contributor.authorMaterka, Andrzej
dc.date.accessioned2021-04-30T11:15:35Z
dc.date.available2021-04-30T11:15:35Z
dc.date.created2020-10-01T11:45:26Z
dc.date.issued2020
dc.PublishedLecture Notes in Computer Science (LNCS). 2020, 12334 72-86.
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11250/2740577
dc.description.abstractSimulation of a dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) multiple sclerosis brain dataset is described. The simulated images in the implemented version have 1×1×1mm3 voxel resolution and arbitrary temporal resolution. Addition of noise and simulation of thick-slice imaging is also possible. Contrast agent (Gd-DTPA) passage through tissues is modelled using the extended Tofts-Kety model. Image intensities are calculated using signal equations of the spoiled gradient echo sequence that is typically used for DCE imaging. We then use the simulated DCE images to study the impact of slice thickness and noise on the estimation of both semi- and fully-quantitative pharmacokinetic features. We show that high spatial resolution images allow significantly more accurate modelling than interpolated low resolution DCE images.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.titleOn the Effect of DCE MRI Slice Thickness and Noise on Estimated Pharmacokinetic Biomarkers – A Simulation Studyen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright Springer Nature Switzerland AG 2020en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.doi10.1007/978-3-030-59006-2_7
dc.identifier.cristin1836087
dc.source.journalLecture Notes in Computer Science (LNCS)en_US
dc.source.4012334
dc.source.pagenumber72-86en_US
dc.identifier.citationLecture Notes in Computer Science (LNCS). 2020, 12334:72-86en_US
dc.source.volume12334en_US


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