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dc.contributor.authorTautz, Lennarten_US
dc.contributor.authorHüllebrand, Markusen_US
dc.contributor.authorSteinmetz, Michaelen_US
dc.contributor.authorVoit, Dirken_US
dc.contributor.authorFrahm, Jensen_US
dc.contributor.authorHennemuth, Anjaen_US
dc.contributor.editorStefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Riederen_US
dc.date.accessioned2017-09-06T07:12:44Z
dc.date.available2017-09-06T07:12:44Z
dc.date.issued2017
dc.identifier.isbn978-3-03868-036-9
dc.identifier.issn2070-5786
dc.identifier.urihttp://dx.doi.org/10.2312/vcbm.20171251
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20171251
dc.description.abstractFunction of the heart, including interventricular septum motion, is influenced by respiration and contraction of the heart muscle. Recent real-time magnetic resonance imaging (MRI) can acquire multi-cycle cardiac data, which enables the analysis of the variation between heart cycles depending on factors such as physical stress or changes in respiration. There are no normal values for this variation in the literature, and there are no established tools for the analysis and exploration of such multi-cycle data available. We propose an analysis and exploration concept that automatically segments the left and right ventricle, extracts motion parameters and allows to interactively explore the results. We tested the concept using nine real-time MRI data sets, including one subject under increasing stress levels and one subject performing a breathing maneuver. All data sets could be automatically processed and then explored successfully, suggesting that our approach can robustly quantify and explore septum thickness in real-time MRI data.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCCS Concepts
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectComputing methodologies
dc.subjectImage segmentation
dc.titleExploration of Interventricular Septum Motion in Multi-Cycle Cardiac MRIen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.description.sectionheadersShape and Models
dc.identifier.doi10.2312/vcbm.20171251
dc.identifier.pages169-178


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