Vortex Detection in 4D MRI Data: Using the Proper Orthogonal Decomposition for Improved Noise-Robustness

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dc.contributor.authorCarnecky, Roberten_US
dc.contributor.authorBrunner, Thomasen_US
dc.contributor.authorBorn, Silviaen_US
dc.contributor.authorWaser, Jürgenen_US
dc.contributor.authorHeine, Christianen_US
dc.contributor.authorPeikert, Ronalden_US
dc.contributor.editorN. Elmqvist and M. Hlawitschka and J. Kennedyen_US
dc.date.accessioned2014-12-16T07:21:10Z
dc.date.available2014-12-16T07:21:10Z
dc.date.issued2014en_US
dc.description.abstractRecent advances in magnetic resonance imaging (MRI) technology enabled the acquisition of time-resolved 3Dblood flow data. Several flow visualization methods have been applied to these data in order to investigate linksbetween cardiovascular diseases and hemodynamic phenomena, such as vortices in the blood flow. In this work,we investigate the use of the proper orthogonal decomposition (POD) for the preprocessing of MRI datasets andstudy its effects with the l2 vortex detection method. By comparing the POD method with the commonly usedGaussian filtering, we show that for comparable filtering strengths, the POD produces qualitatively better results.en_US
dc.description.seriesinformationEuroVis - Short Papersen_US
dc.identifier.isbn978-3-905674-69-9en_US
dc.identifier.urihttps://doi.org/10.2312/eurovisshort.20141169en_US
dc.publisherThe Eurographics Associationen_US
dc.titleVortex Detection in 4D MRI Data: Using the Proper Orthogonal Decomposition for Improved Noise-Robustnessen_US
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