Eduard Groeller

Survey of the Visual Exploration and Analysis of Perfusion Data

Dynamic contrast-enhanced image data (perfusion data) are used to characterize regional tissue perfusion. Perfusion data consist of a sequence of images, acquired after a contrast agent bolus is applied. Perfusion data are used for diagnostic purposes in oncology, ischemic stroke assessment or myocardial ischemia. The diagnostic evaluation of perfusion data is challenging, since the data is complex and exhibits various artifacts, e.g., motion artifacts. We provide an overview on existing methods to analyze, and visualize CT and MR perfusion data. The integrated visualization of several 2D parameter maps, the 3D visualization of parameter volumes and exploration techniques are discussed. An essential aspect in the diagnosis of perfusion data is the correlation between perfusion data and derived time-intensity curves as well as with other image data, in particular with high resolution morphologic image data. We discuss visualization support with respect to the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis and the diagnosis of coronary heart disease.

Visualization of Analytically Defined Dynamical Systems

The visualization of analytically defined dynamical systems is important for a thorough understanding of the underlying system behavior. An overview of theoretical concepts concerning analytically defined dynamical systems is given. Various visualization techniques for dynamical systems are discussed. Three current research directions concerning the visualization of dynamical systems are treated in more detail. These are: texture based techniques, visualization of high-dimensional dynamical systems, and advanced streamsurface representations.

Case study: visualizing various properties of dynamical systems

There is a wide range of visualization techniques for dynamical systems. These methods are used to visualize certain properties as, e.g., stability of fixed points, characteristic changes of velocity, and bifurcations. This paper gives a short introduction to dynamical system and describes several visualization techniques. Some of those are applied to three different dynamical system. The application of different visualization methods to dynamical systems shows how scientific visualization can be used for analyzing the behavior of dynamical systems, and how visualization can make analysis of a dynamical system fast and efficient.

Improving the visualization of electron-microscopy data through optical flow interpolation

Technical developments in neurobiology have reached a point where the acquisition of high resolution images representing individual neurons and synapses becomes possible. For this, the brain tissue samples are sliced using a diamond knife and imaged with electron-microscopy (EM). However, the technique achieves a low resolution in the cutting direction, due to limitations of the mechanical process, making a direct visualization of a dataset difficult. We aim to increase the depth resolution of the volume by adding new image slices interpolated from the existing ones, without requiring modifications to the EM image-capturing method. As classical interpolation methods do not provide satisfactory results on this type of data, the current paper proposes a re-framing of the problem in terms of motion volumes, considering the depth axis as a temporal axis. An optical flow method is adapted to estimate the motion vectors of pixels in the EM images, and this information is used to compute and insert multiple new images at certain depths in the volume. We evaluate the visualization results in comparison with interpolation methods currently used on EM data, transforming the highly anisotropic original dataset into a dataset with a larger depth resolution. The interpolation based on optical flow better reveals neurite structures with realistic undistorted shapes, and helps to easier map neuronal connections.