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dc.contributor.authorEnglund, Rickarden_US
dc.contributor.authorRopinski, Timoen_US
dc.contributor.authorHotz, Ingriden_US
dc.contributor.editorStefan Bruckner and Bernhard Preim and Anna Vilanova and Helwig Hauser and Anja Hennemuth and Arvid Lundervolden_US
dc.date.accessioned2016-09-07T05:37:39Z
dc.date.available2016-09-07T05:37:39Z
dc.date.issued2016
dc.identifier.isbn978-3-03868-010-9
dc.identifier.issn2070-5786
dc.identifier.urihttp://dx.doi.org/10.2312/vcbm.20161274
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20161274
dc.description.abstractBlood flow data from direct measurements (4D flow MRI) or numerical simulations opens new possibilities for the understanding of the development of cardiac diseases. However, before this new data can be used in clinical studies or for diagnosis, it is important to develop a notion of the characteristics of typical flow structures. To support this process we developed a novel blood flow clustering and exploration method. The method builds on the concept of coherent flow structures. Coherence maps for crosssectional slices are defined to show the overall degree of coherence of the flow. In coherent regions the method summarizes the dominant blood flow using a small number of pathline representatives. In contrast to other clustering approaches the clustering is restricted to coherent regions and pathlines with low coherence values, which are not suitable for clustering and thus are not forced into clusters. The coherence map is based on the Finite-time Lyapunov Exponent (FTLE). It is created on selected planes in the inflow respective outflow area of a region of interest. The FTLE value measures the rate of separation of pathlines originating from this plane. Different to previous work using FTLE we do not focus on separating extremal lines but on local minima and regions of low FTLE intensities to extract coherent flow. The coherence map and the extracted clusters serve as basis for the flow exploration. The extracted clusters can be selected and inspected individually. Their flow rate and coherence provide a measure for their significance. Switching off clusters reduces the amount of occlusion and reveals the remaining part of the flow. The non-coherent regions can also be explored by interactive manual pathline seeding in the coherence map.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputer Graphics [I.3.7]
dc.subjectThree Dimensional Graphics and Realism
dc.subjectColor
dc.subjectshading
dc.subjectshadowing
dc.subjectand texture
dc.titleCoherence Maps for Blood Flow Explorationen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.description.sectionheadersVisual Computing for Blood Flow Analysis and Motion Analysis
dc.identifier.doi10.2312/vcbm.20161274
dc.identifier.pages79-88


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