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dc.contributor.authorFossan, Fredrik Eikeland
dc.contributor.authorSturdy, Jacob
dc.contributor.authorMuller, Lucas Omar
dc.contributor.authorStrand, Andreas
dc.contributor.authorBråten, Anders Tjellaug
dc.contributor.authorJørgensen, Arve
dc.contributor.authorWiseth, Rune
dc.contributor.authorHellevik, Leif Rune
dc.date.accessioned2019-10-16T13:02:48Z
dc.date.available2019-10-16T13:02:48Z
dc.date.created2018-11-28T11:16:40Z
dc.date.issued2018
dc.identifier.citationCardiovascular Engineering and Technology. 2018, 9 (4), 597-622.nb_NO
dc.identifier.issn1869-408X
dc.identifier.urihttp://hdl.handle.net/11250/2622598
dc.description.abstractPurpose The main objectives of this study are to validate a reduced-order model for the estimation of the fractional flow reserve (FFR) index based on blood flow simulations that incorporate clinical imaging and patient-specific characteristics, and to assess the uncertainty of FFR predictions with respect to input data on a per patient basis. Methods We consider 13 patients with symptoms of stable coronary artery disease for which 24 invasive FFR measurements are available. We perform an extensive sensitivity analysis on the parameters related to the construction of a reduced-order (hybrid 1D–0D) model for FFR predictions. Next we define an optimal setting by comparing reduced-order model predictions with solutions based on the 3D incompressible Navier–Stokes equations. Finally, we characterize prediction uncertainty with respect to input data and identify the most influential inputs by means of sensitivity analysis. Results Agreement between FFR computed by the reduced-order model and by the full 3D model was satisfactory, with a bias (FFR3D−FFR1D−0DFFR3D−FFR1D−0D) of −0.03(±0.03)−0.03(±0.03) at the 24 measured locations. Moreover, the uncertainty related to the factor by which peripheral resistance is reduced from baseline to hyperemic conditions proved to be the most influential parameter for FFR predictions, whereas uncertainty in stenosis geometry had greater effect in cases with low FFR. Conclusion Model errors related to solving a simplified reduced-order model rather than a full 3D problem were small compared with uncertainty related to input data. Improved measurement of coronary blood flow has the potential to reduce uncertainty in computational FFR predictions significantly.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringer Verlagnb_NO
dc.titleUncertainty quantification and sensitivity analysis for computational FFR estimation in stable coronary artery diseasenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber597-622nb_NO
dc.source.volume9nb_NO
dc.source.journalCardiovascular Engineering and Technologynb_NO
dc.source.issue4nb_NO
dc.identifier.doi10.1007/s13239-018-00388-w
dc.identifier.cristin1636303
dc.relation.projectNotur/NorStore: NN9545Knb_NO
dc.description.localcodeThis is a post-peer-review, pre-copyedit version of an article published in [Cardiovascular Engineering and Technology] Locked until 31.10.2019 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/s13239-018-00388-wnb_NO
cristin.unitcode194,64,45,0
cristin.unitcode194,65,25,0
cristin.unitcode1920,6,0,0
cristin.unitcode1920,4,0,0
cristin.unitnameInstitutt for konstruksjonsteknikk
cristin.unitnameInstitutt for sirkulasjon og bildediagnostikk
cristin.unitnameKlinikk for hjertemedisin
cristin.unitnameKlinikk for bildediagnostikk
cristin.ispublishedtrue
cristin.fulltextpreprint
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