Interactive Formation of Statistical Hypotheses in Diffusion Tensor Imaging

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dc.contributor.authorAbbasloo, Aminen_US
dc.contributor.authorWiens, Vitalisen_US
dc.contributor.authorSchmidt-Wilcke, Tobiasen_US
dc.contributor.authorSundgren, Piaen_US
dc.contributor.authorKlein, Reinharden_US
dc.contributor.authorSchultz, Thomasen_US
dc.contributor.editorKozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgiaen_US
dc.date.accessioned2019-09-03T13:49:02Z
dc.date.available2019-09-03T13:49:02Z
dc.date.issued2019
dc.description.abstractWhen Diffusion Tensor Imaging (DTI) is used in clinical studies, statistical hypothesis testing is the standard approach to establish significant differences between groups, such as patients and healthy controls. However, diffusion tensors contain six degrees of freedom, and the most commonly used univariate tests reduce them to a single scalar, such as Fractional Anisotropy. Multivariate tests that account for the full tensor information have been developed, but have not been widely adopted in practice. Based on analyzing the limitations of existing univariate and multivariate tests, we argue that it is beneficial to use a more flexible, steerable test. Therefore, we introduce a test that can be customized to include any subset of tensor attributes that are relevant to the analysis task at hand. We also present a visual analytics system that supports the exploratory task of customizing it to a specific scenario. Our system closely integrates quantitative analysis with suitable visualizations. It links spatial and abstract views to reveal clusters of strong differences, to relate them to the affected anatomical structures, and to visually compare the results of different tests. A use case is presented in which our system leads to the formation of several new hypotheses about the effects of systemic lupus erythematosus on water diffusion in the brain.en_US
dc.description.sectionheadersVisual Computing for MRI-based Data
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20191229
dc.identifier.isbn978-3-03868-081-9
dc.identifier.issn2070-5786
dc.identifier.pages33-43
dc.identifier.urihttps://doi.org/10.2312/vcbm.20191229
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20191229
dc.publisherThe Eurographics Associationen_US
dc.subjectVisualization application domains
dc.subjectVisual analytics
dc.subjectLife and medical sciences
dc.subjectHealth informatics
dc.titleInteractive Formation of Statistical Hypotheses in Diffusion Tensor Imagingen_US
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VCBM 19: Eurographics Workshop on Visual Computing for Biology and Medicine