Natascha Sauber’s research while affiliated with Max Planck Institute for Informatics and other places

Fiber tracking is a standard tool to estimate the course of major white matter tracts from diffusion tensor magnetic resonance imaging (DT-MRI) data. In this work, we aim at supporting the visual analysis of classical streamlines from fiber tracking by integrating context from anatomical data, acquired by a $T_1$-weighted MRI measurement. To this end, we suggest a novel visualization metaphor, which is based on data-driven deformation of geometry and has been inspired by a technique for anatomical fiber preparation known as Klingler dissection. We demonstrate that our method conveys the relation between streamlines and surrounding anatomical features more effectively than standard techniques like slice images and direct volume rendering. The method works automatically, but its GPU-based implementation allows for additional, intuitive interaction.

We present an approach to visualizing correlations in 3D multifield scalar data. The core of our approach is the computation of correlation fields, which are scalar fields containing the local correlations of subsets of the multiple fields. While the visualization of the correlation fields can be done using standard 3D volume visualization techniques, their huge number makes selection and handling a challenge. We introduce the Multifield-Graph to give an overview of which multiple fields correlate and to show the strength of their correlation. This information guides the selection of informative correlation fields for visualization. We use our approach to visually analyze a number of real and synthetic multifield datasets.

Diffusion tensor imaging is a magnetic resonance imaging method which has gained increasing importance in neuroscience and especially in neurosurgery. It acquires diffusion properties represented by a symmetric 2nd order tensor for each voxel in the gathered dataset. From the medical point of view, the data is of special interest due lo different diffusion characteristics of varying brain tissue allowing conclusions about the underlying structures such as while matter tracts. An obvious way to visualize this data is to focus on the anisotropic areas using the major eigenvector for tractography and rendering lines for visualization of the simulation results. Our approach extends this technique to avoid line representation since lines lead 10 very complex illustrations and furthermore are mistakable. Instead, we generate surfaces wrapping bundles of lines. Thereby, a more intuitive representation of different tracts is achieved.

Direct volume visualization of computer tomography data is based on the mapping of data values to colors and opacities with lookup-tables known as transfer functions (TF). Often, limitations of one-dimensional TF become evident when it comes to the visualization of aneurysms close the skull base. Computer tomography angiography data is used for the 3D-representation of the vessels filled with contrast medium. The reduced intensity differences between osseous tissue and contrast medium lead to strong artifacts and ambiguous visualizations. We introduced the use of bidimensional TFs based on measured intensities and gradient magnitudes for the visualization of aneurysms involving the skull base. The obtained results are clearly superior to a standard approach with one-dimensional TFs. Nevertheless, the additional degree of freedom increases the difficulty involved in creating adequate TFs. In order to address this problem, we introduce automatic adjustment of bidimensional TFs through a registration of respective 2D histograms. Initially, a dataset is set as reference and the information contained in its 2D histogram (intensities and gradient magnitudes) is used to create a TF template which produces a clear visualization of the vessels. When a new dataset is examined, elastic registration of the reference and target 2D histograms is carried out. The resulting free form deformation is then used for the automatic adjustment of the reference TF, in order to automatically obtain a clear volume visualization of the vascular structures within the examined dataset. Results are comparable to manually created TFs. This approach makes it possible to successfully use bidimensional TFs without technical insight and training.

Direct volume visualization has recently gained importance as a tool for the analysis of intracranial aneurysms focused on therapy planning. CT-angiography (CTA) intensities are mapped to color and opacity values by so called one-dimensional transfer functions. In this work, we introduce 3D-visualization of intracranial aneurysms making use of transfer functions based on measured values and gradient magnitudes extracted from the CTA data. Furthermore, we present a tool for the creation of 2D transfer functions in the clinical environment. The visualization application runs on standard PCs equipped with modern 3D graphics cards. Evaluations were carried out on 17 clinical cases from our archive. Clear improvements with respect to standard volume visualization were observed especially in the area of the skull base, where the arteries are difficult to separate from the bone. Effective separation of skull and arteries was achieved even for cases where the critical vascular structures were embedded in osseous tissue. Keywords3D-Visualization-Transfer Function-Aneurysm-Skull Base