Explorative Blood Flow Visualization using Dynamic Line Filtering based on Surface Features. (10th July 2018)

• Record Type: Journal Article
• Authors: Behrendt, B.; Berg, P.; Beuing, O.; Preim, B.; Saalfeld, S.
• Is Part Of: Computer graphics forum. Volume 37:Number 3(2018)
• Page Start: 183
• Page End: 194
• Publication Date: 2018-07-10
• Subjects: Categories and Subject Descriptors (according to ACM CCS) -- I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism—Color, shading, shadowing, and texture -- I.4.8 [Computer Graphics]: Scene Analysis—Shading; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13411
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: Rupture risk assessment is a key to devise patient‐specific treatment plans of cerebral aneurysms. To understand and predict the development of aneurysms and other vascular diseases over time, both hemodynamic flow patterns and their effect on the vessel surface need to be analyzed. Flow structures close to the vessel wall often correlate directly with local changes in surface parameters, such as pressure or wall shear stress. Yet, in many existing applications, the analyses of flow and surface features are either somewhat detached from one another or only globally available. Especially for the identification of specific blood flow characteristics that cause local startling parameters on the vessel surface, like elevated pressure values, an interactive analysis tool is missing. The explorative visualization of flow data is challenging due to the complexity of the underlying data. In order to find meaningful structures in the entirety of the flow, the data has to be filtered based on the respective explorative aim. In this paper, we present a combination of visualization, filtering and interaction techniques for explorative analysis of blood flow with a focus on the relation of local surface parameters and underlying flow structures. Coherent bundles of pathlines can be interactively selected based on their relation to features of the vessel wall and further refined based on their own hemodynamic features. This allows the user to interactively select and explore flow structures locally affecting a certain region on the vessel wall and therefore to understand the cause and effect relationship between these entities. Additionally, multiple selected flow structures can be compared with respect to their quantitative parameters, such as flow speed. We confirmed the usefulness of our approach by conducting an informal interview with two expert neuroradiologists and an expert in flow simulation. In addition, we recorded several insights the neuroradiologists were able to gain with the help of our tool.