Simulation and virtual reality visualization of blood hemodynamics: The Virtual Aneurysm

Intracranial aneurysms are the primary cause of non-traumatic subarachnoid hemorrhage. Difficulties in identifying which aneurysms will grow and rupture arise because the physicians lack important anatomic and hemodynamic information. Through simulation, this data can be captured, but visualization of large simulated data sets becomes cumbersome and tedious, often resulting in visual clutter and ambiguity. To address these visualization issues, we developed an automated algorithm that decomposes the patterns of 3D, unsteady blood flow into behavioral components to reduce the visual complexity while retaining the structure and information of the original data. Our structural approach analyzes sets of pathlines and groups them together based on spatial locality and shape similarity. Adaptive thresholding is used to refine each component grouping to obtain the largest and tightest cluster. These components can then be visualized individually or superimposed together to formulate a rich understanding of the flow patterns in the aneurysm.