2.5D Geometric Mapping of Aortic Blood Flow Data for Cohort Visualization
| dc.contributor.author | Behrendt, Benjamin | en_US |
| dc.contributor.author | Pleuss-Engelhardt, David | en_US |
| dc.contributor.author | Gutberlet, Matthias | en_US |
| dc.contributor.author | Preim, Bernhard | en_US |
| dc.contributor.editor | Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas | en_US |
| dc.date.accessioned | 2021-09-21T08:09:42Z | |
| dc.date.available | 2021-09-21T08:09:42Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Four-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.sectionheaders | The path that blood takes | |
| dc.description.seriesinformation | Eurographics Workshop on Visual Computing for Biology and Medicine | |
| dc.identifier.doi | 10.2312/vcbm.20211348 | |
| dc.identifier.isbn | 978-3-03868-140-3 | |
| dc.identifier.issn | 2070-5786 | |
| dc.identifier.pages | 91-100 | |
| dc.identifier.uri | https://doi.org/10.2312/vcbm.20211348 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/vcbm20211348 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Human centered computing | |
| dc.subject | Visualization toolkits | |
| dc.subject | Information visualization | |
| dc.title | 2.5D Geometric Mapping of Aortic Blood Flow Data for Cohort Visualization | en_US |