Merhof, Dorit
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Fiber Selection from Diffusion Tensor Data based on Boolean Operators
Diffusion tensor imaging (DTI) 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 techniques, which are commonly referred to as fiber tracking. However, the resulting visualization of fibers within the whole brain may be complex and difficult to interpret. For this reason, a novel strategy for selecting specific tract systems based on user-defined regions of interest (ROIs) and Boolean operators is presented in this work. The approach provides ultimate flexibility and is an excellent tool for fiber tract selection and planning in neurosurgery.
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.
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.
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.
Isosurface-based generation of hulls encompassing neuronal pathways
Diffusion tensor imaging provides information about the location of white matter tracts within the human brain. For neurosurgery, this imaging technique is of major interest in order to minimize the risk of postoperative neurological deficits. In preoperative planning, fiber tracking algorithms based on streamline propagation are used in order to reconstruct major fiber tracts. The resulting streamline bundles approximate the course of the underlying white matter structures and indicate their shape and location in 3 dimensions as well as the spatial relation with respect to surrounding anatomy. However, for intraoperative application in combination with the neuronavigation system, these streamline representations are not adequate. Hulls encompassing the streamline bundles are necessary, since the boundary curves of hulls can be superimposed on the operating room (OR) microscope view for guidance in neurosurgery. Methods: In this work, we present a novel hull approach which is based on rasterization and isosurface extraction, combined with surface filtering techniques. The advantages of this approach are its robustness and the possibility to control the tightness of wrapping. Results: The approach makes it possible to generate precise hulls for different tract systems, which can be used as a basis for intraoperative visualization in the OR microscope. Distance measurements further confirm the accuracy of the hulls.
Anisotropic quadrilateral mesh generation : an indirect approach
In this paper a new indirect approach is presented for anisotropic quadrilateral mesh generation based on discrete surfaces. The ability to generate grids automatically had a pervasive influence on many application areas in particularly in the field of Computational Fluid Dynamics. In spite of considerable advances in automatic grid generation there is still potential for better performance and higher element quality. The aim is to generate meshes with less elements which fit some anisotropy criterion to satisfy numerical accuracy while reducing processing times remarkably. The generation of high quality volume meshes using an advancing front algorithm relies heavily on a well designed surface mesh. For this reason this paper presents a new technique for the generation of high quality surface meshes containing a significantly reduced number of elements. This is achieved by creating quadrilateral meshes that include anisotropic elements along a source of anisotropy.
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.
Correction of susceptibility artifacts in diffusion tensor data using non-linear registration
Diffusion tensor imaging can be used to localize major white matter tracts within the human brain. For surgery of tumors near eloquent brain areas such as the pyramidal tract this information is of importance to achieve an optimal resection while avoiding post-operative neurological deficits. However, due to the small bandwidth of echo planar imaging, diffusion tensor images suffer from susceptibility artifacts resulting in positional shifts and distortion. As a consequence, the fiber tracts computed from echo planar imaging data are spatially distorted. We present an approach based on non-linear registration using B´ezier functions to efficiently correct distortions due to susceptibility artifacts. The approach makes extensive use of graphics hardware to accelerate the non-linear registration procedure. An improvement presented in this paper is a more robust and efficient optimization strategy based on simultaneous perturbation stochastic approximation (SPSA). Since the accuracy of non-linear registration is crucial for the value of the presented correction method, two techniques were applied in order to prove the quality of the proposed framework. First, the registration accuracy was evaluated by recovering a known transformation with non-linear registration. Second, landmark-based evaluation of the registration method for anatomical and diffusion tensor data was performed. The registration was then applied to patients with lesions adjacent to the pyramidal tract in order to compensate for susceptibility artifacts. The effect of the correction on the pyramidal tract was then quantified by measuring the position of the tract before and after registration. As a result, the distortions observed in phase encoding direction were most prominent at the cortex and the brainstem. The presented approach allows correcting fiber tract distortions which is an important prerequisite when tractography data are integrated into a stereotactic setup for intra-operative guidance.
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.
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.