Since early times, blood is considered the liquid of life. Indeed, the behavior of flowing blood plays a vital role in the circulatory system, both in health and disease. For most cardiovascular diseases, which currently form the primary cause of death worldwide, blood-flow information can be of great value for diagnosis, and the assessment of treatment options. However, clinical practice primarily relies on morphological indicators to assess disease developments. Even though clinical research has gained a much better understanding of the blood-flow behavior, many aspects remain unexplored.
In recent year, the acquisition of blood-flow information has gone far beyond elementary pulse and blood-pressure measurements. In particular, time-varying volumetric blood-flow velocity fields, also referred to as 4D blood-flow data, can be measured by magnetic resonance imaging (MRI), or modeled through computational fluid dynamics (CFD) simulations. These dense and continuous flow fields contain an abundance of information, which harbor great clinical potential. Clinical research therefore explores these data, aiming for indicators that relate the hemodynamics to pathogenesis.
The complex data can, however, not be inspected using traditional slice-by-slice inspection. Comprehensive flow visualization techniques are required to effectively convey the relevant information, and hence to gain understanding of the hemodynamics. This presentation will provide an overview of the visualization techniques we have investigated for exploration of 4D blood-flow data, ranging from global clustering techniques to local blood-flow probing. We will furthermore set out future challenges for blood-flow visualization research.

*Short Biography*

Roy van Pelt studied Computer Science and Engineering at Eindhoven University of Technology. His graduation project was carried out at the faculty of Biomedical Engineering in 2007, in the Biomedical Image Analysis group, led by professor Bart ter Haar Romeny. His final project, supervised by Anna Vilanova, focused on 3D illustrative visualization using modern consumer graphics hardware. After his graduation, he started his PhD project at the Biomedical Image Analysis group, in collaboration with Philips Healthcare.