Detection and Visual Analysis of Pathological Abnormalities in Diffusion Tensor Imaging with an Anomaly Lens

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dc.contributor.authorBareth, Marloen_US
dc.contributor.authorGroeschel, Samuelen_US
dc.contributor.authorGruen, Johannesen_US
dc.contributor.authorPretzel, Pabloen_US
dc.contributor.authorSchultz, Thomasen_US
dc.contributor.editorHoellt, Thomasen_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorWang, Beien_US
dc.date.accessioned2023-06-10T06:34:34Z
dc.date.available2023-06-10T06:34:34Z
dc.date.issued2023
dc.description.abstractIn clinical practice, Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is usually evaluated by visual inspection of grayscale maps of Fractional Anisotropy or mean diffusivity. However, the fact that those maps only contain part of the information that is captured in DT-MRI implies a risk of missing signs of disease. In this work, we propose a visualization system that supports a more comprehensive analysis with an anomaly score that accounts for the full diffusion tensor information. It is computed by comparing the DT-MRI scan of a given patient to a control group of healthy subjects, after spatial coregistration. Moreover, our system introduces an Anomaly Lens which visualizes how a user-specified region of interest deviates from the controls, indicating which aspects of the tensor (norm, anisotropy, mode, rotation) differ most, whether they are elevated or reduced, and whether their covariation matches the covariances within the control group. Applying our system to patients with metachromatic leukodystrophy clearly indicates regions affected by the disease, and permits their detailed analysis.en_US
dc.description.sectionheaders3D
dc.description.seriesinformationEuroVis 2023 - Short Papers
dc.identifier.doi10.2312/evs.20231041
dc.identifier.isbn978-3-03868-219-6
dc.identifier.pages43-47
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/evs.20231041
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20231041
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Visualization application domains -> Visual analytics; Life and medical sciences -> Health informatics
dc.subjectVisualization application domains
dc.subjectVisual analytics
dc.subjectLife and medical sciences
dc.subjectHealth informatics
dc.titleDetection and Visual Analysis of Pathological Abnormalities in Diffusion Tensor Imaging with an Anomaly Lensen_US
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