Merhof, Dorit
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Generation of hulls encompassing neuronal pathways based on tetrahedralization and 3D alpha shapes
Diffusion tensor imaging provides information about structure and location of white matter tracts within the human brain which is of particular interest for neurosurgery. The reconstruction of neuronal structures from diffusion tensor data is commonly solved by tracking algorithms based on streamline propagation. These approaches generate streamline bundles that approximate the course of neuronal fibers. For medical application, a 3D representation of streamline bundles provides valuable information for pre-operative planning. However, for intra-operative visualization, surfaces wrapping eloquent structures are required for integration into the OR microscope. In order to provide hulls tightly encompassing the neuronal structures obtained from fiber tracking, we propose an approach based on tetrahedralization. This technique reuses the sampling points derived from fiber tracking and therefore provides precise hulls which serve as basis for intra-operative visualization.
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 of diffusion tensor data using evenly spaced streamlines
Diffusion tensor imaging allows investigating white matter structures in vivo which is of particular interest for neurosurgery. A promising approach for the reconstruction of neural pathways are streamline based techniques commonly referred to as fiber tracking. However, due to the diverging nature of tract systems, the density of streamlines varies over the domain without control resulting in sparse areas as well as cramped regions. To overcome this problem, we adapted the concept of evenly spaced streamlines to fiber tracking providing streamlines equally distributed over the domain. Additionally, we incorporated evenly spaced streamlines into region of interest based tracking. We also investigated an adaptive control of the distance between separate streamlines depending on the magnitude of anisotropic diffusion which provides a mechanism to emphasize dominant tract systems.
Reconstruction and visualization of major white matter tracts
Major white matter tracts are bundles of neuronal fibers connecting the cortical brain areas to deep seated regions and periphery. An example is the pyramidal tract, which is responsible for motor function, or the corpus callosum connecting both brain hemispheres. Their preservation during brain surgery is of major importance, in order to avoid postoperative new neurological deficits, such as impairment of motor function.
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, a 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.
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.
DTI-based visualization strategies for the pyramidal tract
With the introduction of diffusion tensor imaging a method became available which is capable to detect major white matter tracts in-vivo. For the visualization of the data several techniques have been developed which, however, show various drawbacks for a comprehensive medical and technical analysis. Although fractional anisotropy maps and streamlines, typically denoted as fiber tracking, are widely used they are suboptimal in several situations of pre- and intraoperative application. Going beyond these standard approaches, several new and more advanced visualization techniques, namely directional volume growing, hulls and hardware-accelerated glyphs are introduced for an improved exploration of the pyramidal tract. The approaches have been evaluated with respect to diagnosis and therapy planning in neurosurgery. Overall, it is shown that the presented strategies for the visualization of diffusion tensor imaging data are capable to significantly support neurosurgical planning and intervention.
Directional volume growing for the extraction of white matter tracts from diffusion tensor data
Diffusion tensor imaging measures diffusion of water in tissue. Within structured tissue, such as neural fiber tracts of the human brain, anisotropic diffusion is observed since the cell membranes of the long cylindric nerves restrict diffusion. Diffusion tensor imaging thus provides information about neural fiber tracts within the human brain which is of major interest for neurosurgery. However, the visualization is a challenging task due to noise and limited resolution of the data. A common visualization strategy of white matter is fiber tracking which utilizes techniques known from flow visualization. The resulting streamlines provide a good impression of the spatial relation of fibers and anatomy. Therefore, they are a valuable supplement for neurosurgical planning. As a drawback, fibers may diverge from the exact path due to numerical inaccuracies during streamline propagation even if higher order integration is used. To overcome this problem, a novel strategy for directional volume growing is presented which enables the extraction of separate tract systems and thus allows to compare and estimate the quality of fiber tracking algorithms. Furthermore, the presented approach is suited to get a more precise representation of the volume encompassing white matter tracts. Thereby, the entire volume potentially containing fibers is provided in contrast to fiber tracking which only shows a more restricted representation of the actual volume of interest. This is of major importance in brain tumor cases where white matter tracts are in the close vicinity of brain tumors. Overall, the presented strategy contributes to make surgical planning safer and more reliable.