Visual Analysis of Aneurysm Data using Statistical Graphics

Meuschke, Monique; Günther, Tobias; Berg, Philipp; Wickenhofer, Ralph; Preim, Bernhard; Lawonn, Kai

doi:10.1109/TVCG.2018.2864509

by Monique Meuschke, Tobias Günther, Philipp Berg, Ralph Wickenhofer, Bernhard Preim, Kai Lawonn

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.

Reference:

Visual Analysis of Aneurysm Data using Statistical Graphics (Monique Meuschke, Tobias Günther, Philipp Berg, Ralph Wickenhofer, Bernhard Preim, Kai Lawonn), In IEEE Transactions on Visualization and Computer Graphics, volume 25, 2019.

Bibtex Entry:

@article{meuschke_visual_2019,
	title = {Visual {Analysis} of {Aneurysm} {Data} using {Statistical} {Graphics}},
	volume = {25},
	issn = {1077-2626},
	doi = {10.1109/TVCG.2018.2864509},
	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.},
	number = {1},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	author = {Meuschke, Monique and Günther, Tobias and Berg, Philipp and Wickenhofer, Ralph and Preim, Bernhard and Lawonn, Kai},
	month = jan,
	year = {2019},
	keywords = {aneurysm, aneurysm data, aneurysm morphology, aneurysm risk, aneurysm ruptures, aneurysms, biomedical MRI, Blood flow, Blood vessels, cardiac arteries, data visualisation, Data visualization, Diseases, haemodynamics, hemodynamic attributes, intracranial arteries, medical image processing, Medical visualizations, parametrization, Shape, statistical analysis, statistical graphics, Surface morphology, suspicious wall regions, Three-dimensional displays, Two dimensional displays, visual analysis, Visualization, wall deformation},
	pages = {997--1007}
}