Merhof, Dorit
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ISMI : a classification index for high angular resolution diffusion imaging
Magnetic resonance diffusion imaging provides a unique insight into the white matter architecture of the brain in vivo. Applications include neurosurgical planning and fundamental neuroscience. Contrary to diffusion tensor imaging (DTI), high angular resolution diffusion imaging (HARDI) is able to characterize complex intra-voxel diffusion distributions and hence provides more accurate information about the true diffusion profile. Anisotropy indices aim to reduce the information of the diffusion probability function to a meaningful scalar representation that classifies the underlying diffusion and thereby the neuronal fiber configuration within a voxel. These indices can be used to answer clinical questions such as the integrity of certain neuronal pathways. Information about the underlying fiber distribution can be beneficial in tractography approaches, reconstructing neuronal pathways using local diffusion orientations. Therefore, an accurate classification of diffusion profiles is of great interest. However, the differentiation between multiple fiber orientations and isotropic diffusion is still a challenging task. In this work, we introduce ISMI, an index which successfully differentiates isotropic diffusion and single and multiple fiber populations. The classifier is based on the orientation distribution function (ODF) resulting from Q-ball imaging. We compare our results with the well-known general fractional anisotropy (GFA) index using a fiber phantom comprising challenging diffusion profiles such as crossing, fanning and kissing fiber configurations and a human brain dataset considering the centrum semiovale. Additionally, we visualize the results directly on the fibers represented by streamtubes using a heat color map.
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 headsup 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.
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.
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.
Intraoperative visualization of the pyramidal tract by diffusion-tensor-imaging-based fiber tracking
Functional neuronavigation allows intraoperative visualization of cortical eloquent brain areas. Major white matter tracts, such as the pyramidal tract, can be delineated by diffusion-tensor-imaging based fiber tracking. These tractography data were integrated into 3-D datasets applied for neuronavigation by rigid registration of the diffusion images with standard anatomical image data so that their course could be superimposed onto the surgical field during resection of gliomas. Intraoperative high-field magnetic resonance imaging was used to compensate for the effects of brain shift, which amounted up to 8 mm. Despite image distortion of echo planar images, which was identified by non-linear registration techniques, navigation was reliable. In none of the 19 patients new postoperative neurological deficits were encountered. Intraoperative visualization of major white matter tracts allows save resection of gliomas near eloquent brain areas. A possible shifting of the pyramidal tract has to be taken into account after major tumor parts are resected.
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.
Visualization strategies for major white matter tracts identified by diffusion tensor imaging for intraoperative use
Streamline representation of major fiber tract systems along with high-resolution anatomical data provides a reliable orientation for the neurosurgeon. For direct visualization of these data 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 mandatory. Integration of fiber tract data into a neuronavigation setup allows removal of tumors adjacent to eloquent brain areas with low morbidity.