Paper Info
Title
Visual Analysis Of Aneurysm Data Using Statistical Graphics
Abstract
This paper presents a framework to explore multi-field data of aneurysms occurring at intracranial and cardiac arteries by using statistical graphics. The rupture of an aneurysm is often a fatal scenario, whereas during treatment serious complications for the patient can occur. Whether an aneurysm ruptures or whether a treatment is successful depends on the interaction of different morphological such as wall deformation and thickness, and hemodynamic attributes like wall shear stress and pressure. Therefore, medical researchers are very interested in better understanding these relationships. However, the required analysis is a time-consuming process, where suspicious wall regions are difficult to detect due to the time-dependent behavior of the data. Our proposed visualization framework enables medical researchers to efficiently assess aneurysm risk and treatment options. This comprises a powerful set of views including 2D and 3D depictions of the aneurysm morphology as well as statistical plots of different scalar fields. Brushing and linking aids the user to identify interesting wall regions and to understand the influence of different attributes on the aneurysm's state. Moreover, a visual comparison of pre- and post-treatment as well as different treatment options is provided. Our analysis techniques are designed in collaboration with domain experts, e.g., physicians, and we provide details about the evaluation.
Year
DOI
Venue
2019
10.1109/TVCG.2018.2864509
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Keywords
Field
DocType
Medical visualizations, aneurysms, blood flow, parametrization
Computer vision,Data visualization,Visual comparison,Brushing and linking,Visualization,Computer science,Aneurysm,Statistical graphics,Artificial intelligence
Journal
Volume
Issue
ISSN
25
1
1077-2626
Citations 
PageRank 
References 
1
0.35
20
Authors
6
Name
Order
Citations
PageRank
M. Meuschke14710.55
Tobias Günther2358.34
P. Berg3206.81
Ralph Wickenhofer411.03
Bernhard Preim51766235.86
Kai Lawonn619929.75