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Visual Analysis of Aneurysm Data using Statistical Graphics.

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    Area of Science:

    • Medical Visualization
    • Biomedical Engineering
    • Cardiovascular Research

    Background:

    • Aneurysm rupture is life-threatening, and treatment success is influenced by complex interactions between morphology and hemodynamics.
    • Analyzing time-dependent aneurysm data for risk assessment is challenging and time-consuming for medical researchers.
    • Understanding these interactions is crucial for improving patient outcomes and treatment strategies.

    Purpose of the Study:

    • To present a novel visualization framework for exploring multi-field aneurysm data.
    • To enable efficient assessment of aneurysm rupture risk and treatment options.
    • To facilitate the understanding of relationships between morphological and hemodynamic attributes.

    Main Methods:

    • Development of a visualization framework integrating 2D/3D morphology depictions and statistical plots.
    • Utilizing brushing and linking techniques for identifying significant wall regions and attribute influences.
    • Incorporating visual comparison of pre- and post-treatment scenarios and diverse treatment options.

    Main Results:

    • The framework enables efficient exploration of time-dependent aneurysm data.
    • Identification of critical wall regions and understanding of attribute influence on aneurysm state.
    • Facilitation of visual comparison for treatment planning and evaluation.

    Conclusions:

    • The proposed visualization framework enhances the analysis of intracranial and cardiac aneurysms.
    • It empowers medical researchers to efficiently assess risks and optimize treatment strategies.
    • Collaborative design with domain experts ensures clinical relevance and applicability.