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We present a new technique for generating surface meshes from a uniform set of discrete samples. Our method extends the well-known marching cubes algorithm used for computing polygonal isosurfaces. While in marching cubes each vertex of a cubic grid cell is binary classified as lying above or below an isosurface, in our approach an arbitrary number of vertex classes can be specified. Consequently the resulting surfaces consist of patches separating volumes of two different classes each. Similar to the marching cubes algorithm all grid cells are traversed and classified according to the number of different vertex classes involved and their arrangement. The solution for each configuration is computed based on a model that assigns probabilities to the vertices and interpolates them. We introduce an automatic method to find a triangulation which approximates the boundary surfaces - implicitly given by our model - in a topological correct way. Look-up tables guarantee a high performance of the algorithm. In medical applications our method can be used to extract surfaces from a 3D segmentation of tomographic images into multiple tissue types. The resulting surfaces are well suited for subsequent volumetric mesh generation, which is needed for simulation as well as visualization tasks. The proposed algorithm provides a robust and unique solution, avoiding ambiguities occuring in other methods. The method is of great significance in modeling and animation too, where it can be used for polygonalization of non-manifold implicit surfaces.

After a short summary on therapy planning and the underlying technologies we discuss quantitative medicine by giving a short overview on medical image data, summarizing some applications of computer based treatment planning, and outlining requirements on medical planning systems. Then we continue with a description of our medical planning system {\sf HyperPlan}. It supports typical working steps in therapy planning, like data aquisition, segmentation, grid generation, numerical simulation and optimization, accompanying these with powerful visualization and interaction techniques.

A new technique for interactive vector field visualization using large numbers of properly illuminated field lines is presented. Taking into account ambient, diffuse, and specular reflection terms as well as transparency and depth cueing, we employ a realistic shading model which significantly increases quality and realism of the resulting images. While many graphics workstations offer hardware support for illuminating surface primitives, usually no means for an accurate shading of line primitives are provided. However, we show that proper illumination of lines can be implemented by exploiting the texture mapping capabilities of modern graphics hardware. In this way high rendering performance with interactive frame rates can be achieved. We apply the technique to render large numbers of integral curves of a vector field. The impression of the resulting images can be further improved by a number of visual enhancements, like transparency and depth-cueing. We also describe methods for controlling the distribution of field lines in space. These methods enable us to use illuminated field lines for interactive exploration of vector fields.

We describe an extension of the line integral convolution method (LIC) for imaging of vector fields on arbitrary surfaces in 3D space. Previous approaches were limited to curvilinear surfaces, i.e.~surfaces which can be parametrized globally using 2D-coordinates. By contrast our method also handles the case of general, possibly multiply connected surfaces. The method works by tesselating a given surface with triangles. For each triangle local euclidean coordinates are defined and a local LIC texture is computed. No scaling or distortion is involved when mapping the texture onto the surface. The characteristic length of the texture remains constant. In order to exploit the texture hardware of modern graphics computers we have developed a tiling strategy for arranging a large number of triangular texture pieces within a single rectangular texture image. In this way texture memory is utilized optimally and even large textured surfaces can be explored interactively.

Hyperthermia Treatment Planning in Clinical Cancer Therapy: Modelling, Simulation and Visualization
(1997)

\noindent The speaker and his co-workers in Scientific Computing and Visualization have established a close cooperation with medical doctors at the Rudolf--Virchow--Klinikum of the Humboldt University in Berlin on the topic of regional hyperthermia. In order to permit a patient--specific treatment planning, a special software system ({\sf\small HyperPlan}) has been developed. \noindent A mathematical model of the clinical system ({\it radio frequency applicator with 8 antennas, water bolus, individual patient body}) involves Maxwell's equations in inhomogeneous media and a so--called bio--heat transfer PDE describing the temperature distribution in the human body. The electromagnetic field and the thermal phenomena need to be computed at a speed suitable for the clinical environment. An individual geometric patient model is generated as a quite complicated tetrahedral ``coarse'' grid (several thousands of nodes). Both Maxwell's equations and the bio--heat transfer equation are solved on that 3D--grid by means of {\em adaptive} multilevel finite element methods, which automatically refine the grid where necessary in view of the required accuracy. Finally optimal antenna parameters for the applicator are determined . \noindent All steps of the planning process are supported by powerful visualization methods. Medical images, contours, grids, simulated electromagnetic fields and temperature distributions can be displayed in combination. A number of new algorithms and techniques had to be developed and implemented. Special emphasis has been put on advanced 3D interaction methods and user interface issues.

Efficient implementations of irregular problems on vector and parallel architectures are generally hard to realize. An important class of problems are Gauß-Seidel iteration schemes applied to irregular data sets. The unstructured data dependences arising there prevent restructuring compilers from generating efficient code for vector or parallel machines. It is shown, how to structure the data dependences by decomposing the underlying data set using graph coloring techniques and by specifying a particular execution order already on the algorithm level. Methods to master the irregularities originating from different types of tasks are proposed. An application is given and some open issues and future developments are discussed.

Line Integral Convolution (LIC) is a powerful technique for generating striking images and animations from vector data. Introduced in 1993, the method has rapidly found many application areas, ranging from computer arts to scientific visualization. Based upon locally filtering an input texture along a curved stream line segment in a vector field, it is able to depict directional information at high spatial resolutions. We present a new method for computing LIC images, which minimizes the total number of stream lines to be computed and thereby reduces computational costs by an order of magnitude compared to the original algorithm. Our methods utilizes fast, error-controlled numerical integrators. Decoupling the characteristic lengths in vector field grid, input texture and output image, it allows to compute filtered images at arbitrary resolution. This feature is of great significance in computer animation as well as in scientific visualization, where it can be used to explore vector data by smoothly enlarging structure of details. We also present methods for improved texture animation, employing constant filter kernels only. To obtain an optimal motion effect, spatial decay of correlation between intensities of distant pixels in the output image has to be controlled. This is achieved by blending different phase shifted box filter animations and by adaptively rescaling the contrast of the output frames.

Sparse LU factorization offers some potential for parallelism, but at a level of very fine granularity. However, most current distributed memory MIMD architectures have too high communication latencies for exploiting all parallelism available. To cope with this, latencies must be avoided by coarsening the granularity and by message fusion. However, both techniques limit the concurrency, thereby reducing the scalability. In this paper, an implementation of a parallel LU decomposition algorithm for linear programming bases is presented for distributed memory parallel computers with noticable communication latencies. Several design decisions due to latencies, including data distribution and load balancing techniques, are discussed. An approximate performance model is set up for the algorithm, which allows to quantify the impact of latencies on its performance. Finally, experimental results for an Intel iPSC/860 parallel computer are reported and discussed.

Segmentation tools in medical imaging are either based on editing geometric curves or on the assignment of region labels to image voxels. While the first approach is well suited to describe smooth contours at subvoxel accuracy, the second approach is conceptually more simple and guarantees a unique classification of image areas. However, contours extracted from labeled images typically exhibit strong staircase artifacts and are not well suited to represent smooth tissue boundaries. In this paper we describe how this drawback can be circumvented by supplementing region labels with additional weights. We integrated our approach into an interactive segmentation system providing a well-defined set of manual and semi-automatic editing tools. All tools update both region labels as well as the corresponding weights simultaneously, thus allowing one to define segmentation results at high resolution. We applied our techniques to generate 3D polygonal models of anatomical structures.

The Monte Carlo simulation of the dynamics of complex molecules produces trajectories with a large number of different configurations to sample configuration space. It is expected that these configurations can be classified into a small number of conformations representing essential changes in the shape of the molecule. We present a method to visualize these conformations by point sets in the plane based on a geometrical distance measure between individual configurations. It turns out that different conformations appear as well-separated point sets. The method is further improved by performing a cluster analysis of the data set. The point-cluster representation is used to control a three-dimensional molecule viewer application to show individual configurations and conformational changes. The extraction of essential coordinates and visualization of molecular shape is discussed.