Classification of Blood Flow Patterns in Cerebral Aneurysms

Meuschke, Monique; Oeltze-Jafra, Steffen; Beuing, Oliver; Preim, Bernhard; Lawonn, Kai

doi:10.1109/TVCG.2018.2834923

by Monique Meuschke, Steffen Oeltze-Jafra, Oliver Beuing, Bernhard Preim, Kai Lawonn

Abstract:

We present a Cerebral Aneurysm Vortex Classification (CAVOCLA) that allows to classify blood flow in cerebral aneurysms. Medical studies assume a strong relation between the progression and rupture of aneurysms and flow patterns. To understand how flow patterns impact the vessel morphology, they are manually classified according to predefined classes. However, manual classifications are time-consuming and exhibit a high inter-observer variability. In contrast, our approach is more objective and faster than manual methods. The classification of integral lines, representing steady or unsteady blood flow, is based on a mapping of the aneurysm surface to a hemisphere by calculating polar-based coordinates. The lines are clustered and for each cluster a representative is calculated. Then, the polar-based coordinates are transformed to the representative as basis for the classification. Classes are based on the flow complexity. The classification results are presented by a detail-on-demand approach using a visual transition from the representative over an enclosing surface to the associated lines. Based on seven representative datasets, we conduct an informal interview with five domain experts to evaluate the system. They confirmed that CAVOCLA allows for a robust classification of intra-aneurysmal flow patterns. The detail-on-demand visualization enables an efficient exploration and interpretation of flow patterns.

Reference:

Classification of Blood Flow Patterns in Cerebral Aneurysms (Monique Meuschke, Steffen Oeltze-Jafra, Oliver Beuing, Bernhard Preim, Kai Lawonn), In IEEE Transactions on Visualization and Computer Graphics, volume 25, 2019.

Bibtex Entry:

@article{meuschke_classification_2019,
	title = {Classification of {Blood} {Flow} {Patterns} in {Cerebral} {Aneurysms}},
	volume = {25},
	issn = {1077-2626},
	doi = {10.1109/TVCG.2018.2834923},
	abstract = {We present a Cerebral Aneurysm Vortex Classification (CAVOCLA) that allows to classify blood flow in cerebral aneurysms. Medical studies assume a strong relation between the progression and rupture of aneurysms and flow patterns. To understand how flow patterns impact the vessel morphology, they are manually classified according to predefined classes. However, manual classifications are time-consuming and exhibit a high inter-observer variability. In contrast, our approach is more objective and faster than manual methods. The classification of integral lines, representing steady or unsteady blood flow, is based on a mapping of the aneurysm surface to a hemisphere by calculating polar-based coordinates. The lines are clustered and for each cluster a representative is calculated. Then, the polar-based coordinates are transformed to the representative as basis for the classification. Classes are based on the flow complexity. The classification results are presented by a detail-on-demand approach using a visual transition from the representative over an enclosing surface to the associated lines. Based on seven representative datasets, we conduct an informal interview with five domain experts to evaluate the system. They confirmed that CAVOCLA allows for a robust classification of intra-aneurysmal flow patterns. The detail-on-demand visualization enables an efficient exploration and interpretation of flow patterns.},
	number = {7},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	author = {Meuschke, Monique and Oeltze-Jafra, Steffen and Beuing, Oliver and Preim, Bernhard and Lawonn, Kai},
	month = jul,
	year = {2019},
	keywords = {aneurysm, aneurysm surface, aneurysms, Blood, Blood flow, blood flow patterns, Blood vessels, brain, CAVOCLA, Cerebral Aneurysm Vortex Classification, classification, Computational fluid dynamics, computerised tomography, Data visualization, Diseases, flow complexity, flow visualisation, haemodynamics, hemodynamics, high interobserver variability, image classification, integral lines, intraaneurysmal flow patterns, manual classifications, medical image processing, Medical visualizations, parametrization, polar-based coordinates, robust classification, steady blood flow, Surface morphology, Two dimensional displays, unsteady blood flow, vessel morphology, visual transition, Visualization},
	pages = {2404--2418}
}