Methods for immersive visual analysis of structural brain data
Hänel, Claudia; Kuhlen, Torsten (Thesis advisor); Preim, Bernhard (Thesis advisor)
Aachen : RWTH Aachen University (2021, 2022)
Dissertation / PhD Thesis
Dissertation, RWTH Aachen University, 2021
The visual analysis of structural brain data is an important method to understand the basics of anatomy, relationships of structures, and functionality of the brain. While the data are three-dimensional by their nature, many visual analysis tools focus on two-dimensional visualization. This thesis emphasizes the spatial aspect of the data and presents methods for a valuable three-dimensional visualization that can support neuroscientists in their everyday work. In order to address the heterogeneity of available structural brain data, three categories are considered: small-scale brain atlas, time series, and large-scale data. For these, this thesis presents interactive methods for visual analysis processes. In order to retain the spatial orientation, depth cues like additional anatomical slices or superimposed brain structures are considered one important aspect for the three-dimensional visualization. Furthermore, a distinctive significance of this thesis is the consideration of Immersive Virtual Environments (IVEs) as a visualization platform. In contrast to desktop environments, the spatial perception is enhanced due to the natural three-dimensional perception based on stereoscopic rendering and head tracking. This simplifies the spatial orientation in the data set and is found to be a beneficial, complementary approach by cooperating neuroscientists. Accordingly, the user interaction and experience with the presented visual analysis tools are designed to be user-friendly in desktop and immersive environments. Therefore, this thesis presents two studies on optimizing the user experience for volume rendering applications in IVEs, which find a trade-off between visual quality and interactivity. The thesis concludes with a prototype for provenance tracking in order to go further beyond a pure visualization work and provide an additional way to gain insight into the data.