2.5D Geometric Mapping of Aortic Blood Flow Data for Cohort Visualization

dc.contributor.authorBehrendt, Benjaminen_US
dc.contributor.authorPleuss-Engelhardt, Daviden_US
dc.contributor.authorGutberlet, Matthiasen_US
dc.contributor.authorPreim, Bernharden_US
dc.contributor.editorOeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomasen_US
dc.date.accessioned2021-09-21T08:09:42Z
dc.date.available2021-09-21T08:09:42Z
dc.date.issued2021
dc.description.abstractFour-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows for a non-invasive acquisition of timeresolved blood flow measurements, providing a valuable aid to clinicians and researchers seeking a better understanding of the interrelation between pathologies of the cardiovascular system and changes in blood flow patterns. Such research requires extensive analysis and comparison of blood flow data within and between different patient cohorts representing different age groups, genders and pathologies. However, a direct comparison between large numbers of datasets is not feasible due to the complexity of the data. In this paper, we present a novel approach to normalize aortic 4D PC-MRI datasets to enable qualitative and quantitative comparisons. We define normalized coordinate systems for the vessel surface as well as the intravascular volume, allowing for the computation of quantitative measures between datasets for both hemodynamic surface parameters as well as flow or pressure fields. To support the understanding of the geometric deformations involved in this process, individual transformations can not only be toggled on or off, but smoothly transitioned between anatomically faithful and fully abstracted states. In an informal interview with an expert radiologist, we confirm the usefulness of our technique. We also report on initial findings from exploring a database of 138 datasets consisting of both patient and healthy volunteers.en_US
dc.description.sectionheadersThe path that blood takes
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20211348
dc.identifier.isbn978-3-03868-140-3
dc.identifier.issn2070-5786
dc.identifier.pages91-100
dc.identifier.urihttps://doi.org/10.2312/vcbm.20211348
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20211348
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman centered computing
dc.subjectVisualization toolkits
dc.subjectInformation visualization
dc.title2.5D Geometric Mapping of Aortic Blood Flow Data for Cohort Visualizationen_US
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