Parameter Sensitivity and Uncertainty Visualization in DTI

dc.contributor.authorSiddiqui, Faizanen_US
dc.contributor.authorHöllt, Thomasen_US
dc.contributor.authorVilanova, Annaen_US
dc.contributor.editorKrone, Michaelen_US
dc.contributor.editorLenti, Simoneen_US
dc.contributor.editorSchmidt, Johannaen_US
dc.date.accessioned2022-06-02T15:29:22Z
dc.date.available2022-06-02T15:29:22Z
dc.date.issued2022
dc.description.abstractDiffusion Tensor Imaging is a powerful technique that provides a unique insight into the complex structure of the brain's white matter. However, several sources of uncertainty limit its widespread use. Data and modeling errors arise due to acquisition noise and modeling transformations. Moreover, the sensitivities of the user-defined parameters and region definitions are not usually evaluated, a small change in these parameters can add large variations in the results. Without showing these uncertainties any visualization of DTI data can potentially be misleading. In our work, we develop a visual analytic tool that provides insight into the accumulated uncertainty in the visualization pipeline. The primary goal of this project is to develop an efficient visualization strategy that will assist the end-user in making critical decisions and make fiber tracking analysis less cumbersome and more reliable, a crucial step towards adoption in the neurosurgical workflow.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2022 - Posters
dc.identifier.doi10.2312/evp.20221138
dc.identifier.isbn978-3-03868-185-4
dc.identifier.pages131-133
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/evp.20221138
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evp20221138
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleParameter Sensitivity and Uncertainty Visualization in DTIen_US
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