Department of Simulation and Graphics, within the group Visualization, University of Magdeburg, Germany.
IEEE Trans Vis Comput Graph. 2011 Dec;17(12):2183-92. doi: 10.1109/TVCG.2011.243.
Blood flow and derived data are essential to investigate the initiation and progression of cerebral aneurysms as well as their risk of rupture. An effective visual exploration of several hemodynamic attributes like the wall shear stress (WSS) and the inflow jet is necessary to understand the hemodynamics. Moreover, the correlation between focus-and-context attributes is of particular interest. An expressive visualization of these attributes and anatomic information requires appropriate visualization techniques to minimize visual clutter and occlusions. We present the FLOWLENS as a focus-and-context approach that addresses these requirements. We group relevant hemodynamic attributes to pairs of focus-and-context attributes and assign them to different anatomic scopes. For each scope, we propose several FLOWLENS visualization templates to provide a flexible visual filtering of the involved hemodynamic pairs. A template consists of the visualization of the focus attribute and the additional depiction of the context attribute inside the lens. Furthermore, the FLOWLENS supports local probing and the exploration of attribute changes over time. The FLOWLENS minimizes visual cluttering, occlusions, and provides a flexible exploration of a region of interest. We have applied our approach to seven representative datasets, including steady and unsteady flow data from CFD simulations and 4D PC-MRI measurements. Informal user interviews with three domain experts confirm the usefulness of our approach.
血流及其衍生数据对于研究颅内动脉瘤的发生和进展及其破裂风险至关重要。为了了解血流动力学,需要有效地可视化多个血流动力学属性,如壁面切应力 (WSS) 和流入射流。此外,焦点和上下文属性之间的相关性也特别有趣。这些属性和解剖信息的富有表现力的可视化需要适当的可视化技术来最小化视觉混乱和遮挡。我们提出了 FLOWLENS,作为一种关注焦点和上下文的方法,满足了这些要求。我们将相关的血流动力学属性组合成一对焦点和上下文属性,并将它们分配到不同的解剖范围内。对于每个范围,我们提出了几种 FLOWLENS 可视化模板,以提供对涉及的血流对的灵活视觉过滤。模板由焦点属性的可视化和镜头内上下文属性的附加描述组成。此外,FLOWLENS 支持局部探测和随时间探索属性变化。FLOWLENS 最大限度地减少了视觉混乱和遮挡,并提供了对感兴趣区域的灵活探索。我们已经将我们的方法应用于七个具有代表性的数据集,包括 CFD 模拟和 4D PC-MRI 测量的稳态和非稳态血流数据。与三位领域专家的非正式用户访谈证实了我们方法的有用性。