Frank Enders

Hybrid Visualization for White Matter Tracts using Triangle Strips and Point Sprites

Diffusion tensor imaging is of high value in neurosurgery, providing information about the location of white matter tracts in the human brain. For their reconstruction, streamline techniques commonly referred to as fiber tracking model the underlying fiber structures and have therefore gained interest. To meet the requirements of surgical planning and to overcome the visual limitations of line representations, a new real-time visualization approach of high visual quality is introduced. For this purpose, textured triangle strips and point sprites are combined in a hybrid strategy employing GPU programming. The triangle strips follow the fiber streamlines and are textured to obtain a tube-like appearance. A vertex program is used to orient the triangle strips towards the camera. In order to avoid triangle flipping in case of fiber segments where the viewing and segment direction are parallel, a correct visual representation is achieved in these areas by chains of point sprites. As a result, high quality visualization similar to tubes is provided allowing for interactive multimodal inspection. Overall, the presented approach is faster than existing techniques of similar visualization quality and at the same time allows for real-time rendering of dense bundles encompassing a high number of fibers, which is of high importance for diagnosis and surgical planning

Visualization of white matter tracts with wrapped streamlines

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.

GPU-based hyperstreamlines for diffusion tensor imaging

We propose a new approach for the visualization of hyperstreamlines, which offers potential for better scalability than the conventional polygon-based approach. Our method circumvents the bandwidth bottleneck between the CPU and GPU by transmitting a small set of parameters for each tube segment and generates the surface directly on the GPU using the classical sphere tracing approach. This reduces the load on the CPU that would otherwise need to provide a suitable level-of-detail representation of the scene, while offering even higher quality in the resulting surfaces since every fragment is traced individually. We demonstrate the effectiveness of this approach by comparing it to the performance and output of conventional visualization tools in the application area of diffusion tensor imaging of human brain MR scans. The method presented here can also be utilized to generate other types of surfaces on the GPU that are too complex to handle with direct ray casting and can therefore be adapted for other applications.

Fast and accurate connectivity analysis between functional regions based on DT-MRI

Diffusion tensor and functional MRI data provide insight into function and structure of the human brain. However, connectivity analysis between functional areas is still a challenge when using traditional fiber tracking techniques. For this reason, alternative approaches incorporating the entire tensor information have emerged. Based on previous research employing pathfinding for connectivity analysis, we present a novel search grid and an improved cost function which essentially contributes to more precise paths. Additionally, implementation aspects are considered making connectivity analysis very efficient which is crucial for surgery planning. In comparison to other algorithms, the presented technique is by far faster while providing connections of comparable quality. The clinical relevance is demonstrated by reconstructed connections between motor and sensory speech areas in patients with lesions located in between.

Visualization strategies for major white matter tracts for intraoperative use

Streamline representation of major fiber tract systems along with high-resolution anatomical data provides a reliable orientation for the neurosurgeon. For intraoperative visualization of these data either on navigation screens near the surgical field or directly in the surgical field applying heads-up displays of operating microscopes, wrapping of all streamlines of interest to render an individual object representing the whole fiber bundle is the most suitable representation. Integration of fiber tract data into a neuronavigation setup allows removal of tumors adjacent to eloquent brain areas with low morbidity.

GPU accelerated normalized mutual information and B-spline transformation

Visualization of multimodal images in medicine and other application areas requires correct and efficient registration. Optimally, the alignment operation is made an integral part of the rendering process. Voxel based approaches using mutual information ensure high quality similarity measurement. As a limiting factor, high computational load is caused since for every iteration of the optimization procedure one volume is transformed into the coordinate system of the other, a 2D histogram is generated and mutual information is computed. The expensive trilinear interpolation operations are well supported by 3D texture mapping hardware. However, existing strategies compute the histogram and mutual information on the CPU and thus require a cost intensive data transfer. Overcoming this considerable bottleneck, we introduce a new approach that efficiently supports all computations on modern graphics cards. This makes expensive data transfers from GPU to main memory dispensable. Due to its modularity, the presented approach can be integrated into general frameworks. As a major result, the speed improvement over existing GPU-CPU strategies amounts to a factor of 4 and over pure conventional CPU techniques to more than a factor of 10. Overall, the suggested strategy contributes considerably to integration of multimodal registration directly into interactive volume visualization.

Hardware-accelerated glyph based visualization of major white matter tracts for analysis of brain tumors

Visualizing diffusion tensor imaging data has recently gained increasing importance. The data is of particular interest for neurosurgeons since it allows analyzing the location and topology of major white matter tracts such as the pyramidal tract. Various approaches such as fractional anisotropy, fiber tracking and glyphs have been introduced but many of them suffer from ambiguous representations of important tract systems and the related anatomy. Furthermore, there is no information about the reliability of the presented visualization. However, this information is essential for neurosurgery. This work proposes a new approach of glyph visualization accelerated with consumer graphics hardware showing a maximum of information contained in the data. Especially, the probability of major white matter tracts can be assessed from the shape and the color of the glyphs. Integrating direct volume rendering of the underlying anatomy based on 3D texture mapping and a special hardware accelerated clipping strategy allows more comprehensive evaluation of important tract systems in the vicinity of a tumor and provides further valuable insights. Focusing on hardware acceleration wherever possible ensures high image quality and interactivity, which is essential for clinical application. Overall, the presented approach makes diagnosis and therapy planning based on diffusion tensor data more comprehensive and allows better assessment of major white matter tracts.

Color-encoded distance visualization of cranial nerve-vessel contacts

PurposeVisualization of pathological contact between cranial nerves and vascular structures at the surface of the brainstem is important for diagnosis and treatment of neurovascular compression (NVC) syndromes. We developed a method for improved visualization of this abnormality.MethodsDistance fields were computed using preoperative MRI scans of individuals with NVC syndromes to support the topological representation of brainstem surface structures with quantitative information. Polygonal models of arteries, cranial nerves and the brainstem were generated using segmented T2 weighted MR data. After color-coding the polygonal models with the respective distances, enhanced color visualization of vessel-nerve locations with possible contacts was achieved.ResultsThe proposed method was implemented and applied to surgical planning in a dozen cases of NVC syndrome. Two selected cases were chosen to demonstrate the feasibility and subjective improvement provided by our visualization technique. Expert neurosurgeons found the improvement valuable and useful for these cases.ConclusionColor-encoded distance information significantly improves the perceptibility of potential nerve-vessel contacts. This method contributes to a better understanding of the complex anatomical situation at the surface of the brainstem and assists in planning of surgery.

Streamline Visualization of Diffusion Tensor Data Based on Triangle Strips

For the visualization of diffusion tensor imaging data, different approaches have been presented such as scalar metrics, glyphs or streamlines. Thereby, streamline techniques commonly referred to as fiber tracking provide a comprehensive and intuitive representation. For this reason, they are preferably applied for preoperative planning. The visualization of streamlines is solved by rendering lines or tubes to achieve even more significant results. However, the number of streamlines for a tracking of the whole brain or very dense tract systems may be immense, making a mesh-based tube visualization inefficient. To overcome this problem, we developed an alternative visualization technique for tubes by using textured triangle strips.

Neuronal fiber connections based on A*-pathfinding

Diffusion tensor imaging has shown potential in providing information about the location of white matter tracts within the human brain. Based on this data, a novel approach is presented establishing connectivity between functional regions using pathfinding. The probability distribution function of the local tensor thereby controls the state space search performed by pathfinding. Additionally, it serves as an indicator for the reliability of the computed paths visualized by color encoding. Besides the capability to handle noisy data, the probabilistic nature of the approach is also able to cope with crossing or branching fibers. The algorithm thus guarantees to establish a connection between cortical regions and on the same hand provides information about the probability of the obtained connection. This approach is especially useful for investigating the connectivity between certain centers of the brain as demonstrated by reconstructed connections between motor and sensory speech areas.