dc.contributor.author | Jurek, Jakub | |
dc.contributor.author | Reisæter, Lars Anders Rokne | |
dc.contributor.author | Kocinski, Marek | |
dc.contributor.author | Materka, Andrzej | |
dc.date.accessioned | 2021-04-30T11:15:35Z | |
dc.date.available | 2021-04-30T11:15:35Z | |
dc.date.created | 2020-10-01T11:45:26Z | |
dc.date.issued | 2020 | |
dc.Published | Lecture Notes in Computer Science (LNCS). 2020, 12334 72-86. | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | https://hdl.handle.net/11250/2740577 | |
dc.description.abstract | Simulation 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.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.title | On the Effect of DCE MRI Slice Thickness and Noise on Estimated Pharmacokinetic Biomarkers – A Simulation Study | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | Copyright Springer Nature Switzerland AG 2020 | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |
dc.identifier.doi | 10.1007/978-3-030-59006-2_7 | |
dc.identifier.cristin | 1836087 | |
dc.source.journal | Lecture Notes in Computer Science (LNCS) | en_US |
dc.source.40 | 12334 | |
dc.source.pagenumber | 72-86 | en_US |
dc.identifier.citation | Lecture Notes in Computer Science (LNCS). 2020, 12334:72-86 | en_US |
dc.source.volume | 12334 | en_US |