Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Interactive visual analysis of perfusion data.

Steffen Oeltze1, Helmut Doleisch, Helwig Hauser

  • 1Department of Simulation and Graphics, University of Magdeburg, Germany. stoeltze@isg.cs.uni-magdeburg.de

IEEE Transactions on Visualization and Computer Graphics
|October 31, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

CrossSet: Unveiling the Complex Interplay of Two Set-typed Dimensions in Multivariate Data.

IEEE transactions on visualization and computer graphics·2025
Same author

Understanding Aortic Dissection Hemodynamics: Evaluating Adapted Smoke Surfaces Against Streakline-Based Techniques.

IEEE transactions on visualization and computer graphics·2025
Same author

Virtual reality eye movement training for neglect rehabilitation.

International journal of clinical and health psychology : IJCHP·2025
Same author

Visualization support for remote collaborative aneurysm treatment planning.

International journal of computer assisted radiology and surgery·2025
Same author

Semi-automatic segmentation of elongated interventional instruments for online calibration of C-arm imaging system.

International journal of computer assisted radiology and surgery·2025
Same author

A survey of intracranial aneurysm detection and segmentation.

Medical image analysis·2025
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
See all related articles

This study introduces an interactive visual analysis method to improve the evaluation of dynamic perfusion imaging data. Our approach aids in earlier disease detection by simplifying complex medical image analysis for better diagnosis.

Area of Science:

  • Medical Imaging
  • Data Visualization
  • Biomedical Engineering

Background:

  • Perfusion data, dynamic medical images reflecting regional blood flow, offer significant diagnostic potential for early disease detection.
  • Current widespread use of perfusion data is limited by inefficient evaluation methods, hindering complex multi-field data analysis.

Purpose of the Study:

  • To present an interactive visual analysis approach for evaluating complex perfusion data.
  • To integrate statistical methods and interactive feature specification for enhanced diagnostic evaluation.

Main Methods:

  • Utilized Correlation Analysis and Principal Component Analysis (PCA) for dimension reduction and understanding inter-parameter relationships.
  • Implemented multiple, linked views for interactive feature definition via multi-dimensional brushing.

Related Experiment Videos

  • Established a focus+context visualization style in 3D, linking feature specifications across all views.

    • Main Results:

    • The developed approach facilitates the reduction of data complexity and guides users to suspicious areas in perfusion datasets.
    • Demonstrated the approach's utility across major clinical applications: ischemic stroke, breast tumor, and coronary heart disease (CHD) diagnosis.
    • Highlighted that the significance of perfusion parameters is highly dependent on patient-specific factors, scanning parameters, and data pre-processing.

    Conclusions:

    • The interactive visual analysis approach enhances the diagnostic evaluation of perfusion data by simplifying complexity.
    • The method shows promise for improving early disease detection and diagnosis across various medical fields.
    • Further research should consider patient variability and technical parameters for optimal application of perfusion analysis.