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dc.date.accessioned2019-12-08T20:08:16Z
dc.date.available2019-12-08T20:08:16Z
dc.date.created2018-05-23T19:22:00Z
dc.date.issued2018
dc.identifier.citationFinsberg, Henrik Nicolay Xi, Ce Tan, Ju Le Zhong, Liang Genet, Martin Sundnes, Joakim Lee, Lik Chuan Wall, Samuel T . Efficient estimation of personalized biventricular mechanical function employing gradient‐based optimization. International Journal for Numerical Methods in Biomedical Engineering. 2018, 34(7)
dc.identifier.urihttp://hdl.handle.net/10852/71417
dc.description.abstractIndividually personalized computational models of heart mechanics can be used to estimate important physiological and clinically‐relevant quantities that are difficult, if not impossible, to directly measure in the beating heart. Here, we present a novel and efficient framework for creating patient‐specific biventricular models using a gradient‐based data assimilation method for evaluating regional myocardial contractility and estimating myofiber stress. These simulations can be performed on a regular laptop in less than 2 h and produce excellent fit between measured and simulated volume and strain data through the entire cardiac cycle. By applying the framework using data obtained from 3 healthy human biventricles, we extracted clinically important quantities as well as explored the role of fiber angles on heart function. Our results show that steep fiber angles at the endocardium and epicardium are required to produce simulated motion compatible with measured strain and volume data. We also find that the contraction and subsequent systolic stresses in the right ventricle are significantly lower than that in the left ventricle. Variability of the estimated quantities with respect to both patient data and modeling choices are also found to be low. Because of its high efficiency, this framework may be applicable to modeling of patient specific cardiac mechanics for diagnostic purposes.
dc.languageEN
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleEfficient estimation of personalized biventricular mechanical function employing gradient‐based optimization
dc.typeJournal article
dc.creator.authorFinsberg, Henrik Nicolay
dc.creator.authorXi, Ce
dc.creator.authorTan, Ju Le
dc.creator.authorZhong, Liang
dc.creator.authorGenet, Martin
dc.creator.authorSundnes, Joakim
dc.creator.authorLee, Lik Chuan
dc.creator.authorWall, Samuel T
cristin.unitcode185,15,5,0
cristin.unitnameInstitutt for informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin1586300
dc.identifier.bibliographiccitationinfo: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=34&rft.spage=&rft.date=2018
dc.identifier.jtitleInternational Journal for Numerical Methods in Biomedical Engineering
dc.identifier.volume34
dc.identifier.issue7
dc.identifier.pagecount20
dc.identifier.doihttps://doi.org/10.1002/cnm.2982
dc.identifier.urnURN:NBN:no-74526
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn2040-7939
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/71417/1/Finsberg_et_al-2018-International_Journal_for_Numerical_Methods_in_Biomedical_Engineering%2B%25281%2529.pdf
dc.type.versionPublishedVersion
cristin.articleide2982


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