dc.date.accessioned | 2022-03-02T17:56:36Z | |
dc.date.available | 2022-03-02T17:56:36Z | |
dc.date.created | 2021-10-16T13:29:22Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Koch, Timo Flemisch, Bernd Rainer, Helmig Wiest, Roland Obrist, Dominik . A multiscale subvoxel perfusion model to estimate diffusive capillary wall conductivity in multiple sclerosis lesions from perfusion MRI data. International Journal for Numerical Methods in Biomedical Engineering. 2020 | |
dc.identifier.uri | http://hdl.handle.net/10852/91709 | |
dc.description.abstract | We propose a new mathematical model to learn capillary leakage coefficients from dynamic susceptibility contrast MRI data. To this end, we derive an embedded mixed-dimension flow and transport model for brain tissue perfusion on a subvoxel scale. This model is used to obtain the contrast agent concentration distribution in a single MRI voxel during a perfusion MRI sequence. We further present a magnetic resonance signal model for the considered sequence including a model for local susceptibility effects. This allows modeling MR signal-time curves that can be compared with clinical MRI data. The proposed model can be used as a forward model in the inverse modeling problem of inferring model parameters such as the diffusive capillary wall conductivity. Acute multiple sclerosis lesions are associated with a breach in the integrity of the blood-brain barrier. Applying the model to perfusion MR data of a patient with acute multiple sclerosis lesions, we conclude that diffusive capillary wall conductivity is a good indicator for characterizing activity of lesions, even if other patient-specific model parameters are not well-known. | |
dc.language | EN | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | A multiscale subvoxel perfusion model to estimate diffusive capillary wall conductivity in multiple sclerosis lesions from perfusion MRI data | |
dc.type | Journal article | |
dc.creator.author | Koch, Timo | |
dc.creator.author | Flemisch, Bernd | |
dc.creator.author | Rainer, Helmig | |
dc.creator.author | Wiest, Roland | |
dc.creator.author | Obrist, Dominik | |
cristin.unitcode | 185,15,13,15 | |
cristin.unitname | Mekanikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 1946394 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=International Journal for Numerical Methods in Biomedical Engineering&rft.volume=&rft.spage=&rft.date=2020 | |
dc.identifier.jtitle | International Journal for Numerical Methods in Biomedical Engineering | |
dc.identifier.volume | 36 | |
dc.identifier.issue | 2 | |
dc.identifier.doi | https://doi.org/10.1002/cnm.3298 | |
dc.identifier.urn | URN:NBN:no-94282 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 2040-7939 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/91709/1/KochetalIJNMBE2020.pdf | |
dc.type.version | PublishedVersion | |
cristin.articleid | e3298 | |