Vr High-quality Volume Rendering of Projection-based Volumetric Data

Volume rendering techniques are conventionally classified into two categories represented by direct and indirect methods. Indirect methods require to transform the initial volumetric model into an intermediate geometrical model in order to efficiently visualize it. In contrast, direct volume-rendering (DVR) methods can directly process the volumetric data. Modern 3D scanning technologies, like CT or MRI, usually provide data as a set of samples on rectilinear grid points, which are computed from the measured projections by discrete tomographic reconstruction. Therefore the set of these reconstructed samples can already be considered as an intermediate volume representation. In this diploma thesis a new paradigm for direct direct volume rendering (DVR) is introduced, which does not even require a rectilinear grid, since it is based on an immediate processing of the measured projections. Arbitrary samples for ray casting are reconstructed from the projections by using the Filtered Back-Projection algorithm. The method presented in this thesis removes an unnecessary and lossy resampling step from the classical volume rendering pipeline. Thus, it provides much higher accuracy than traditional grid-based resampling techniques do. Furthermore a novel high-quality gradient estimation scheme, which is also based on the Filtered Back-Projection algorithm is presented. Finally we introduce a hierarchical space partitioning approach for projection-based volumetric data, which is used to accelerate DVR.