Modern 3D visualization environments for medical image data provide high interactivity and flexibility but
depend on the expert knowledge and the experience of the user with respect to the software application. The
definition of the visualization parameters is a manual time-consuming process and as a result, inter-patient
or inter-study comparisons are extremely difficult. To overcome these drawbacks in case of the analysis and
diagnosis of pathologies, standardization of 3D visualization is an important issue. For this purpose automatically
generated digital video sequences can be used to convey the most important information contained in the data.
In this paper, we present an improvement of our existing web-based service which is now able to calculate the
video sequences in much shorter time exploiting the power of a GPU-cluster. The system requires to transfer a
medical volume dataset from an arbitrary computer connected via Internet and sends back a number of video
files automatically generated with direct volume rendering. To achieve an optimal load balancing of the available
resources, the tasks of automatic adjustment of transfer functions, volume rendering, and video encoding are
divided into small sub-requests, which are distributed to the different cluster nodes in order to be performed in
parallel. An additional preview mode, which renders a number of dedicated frames, provides a direct feedback
and quick overview. For the evaluation, we were focusing on the analysis of intracranial aneurysms and were
able to show that the system can be successfully applied. Further on, the system was developed in a way that
allows easy integration of other analysis tasks.
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.
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.
One of the most important applications of direct volume rendering is
the visualization of labeled medical data. Explicit segmentation of
embedded subvolumes allows a clear separation of neighboring
substructures in the same range of intensity values, which can then be
used for implicit segmentation of fine structures using transfer
functions. Nevertheless, the hard label boundaries of explicitly
segmented structures lead to voxelization artifacts. Pixel-resolution
linear filtering can not solve this problem effectively. In order to
render soft label boundaries for explicitly segmented objects, we have
successfully applied a smoothing algorithm based on gradients of the
volumetric label data as a preprocessing step. A 3D-texture based
rendering approach was implemented, where volume labels are
interpolated independently of each other using the graphics
hardware. Thereby, correct trilinear interpolation of four subvolumes
is obtained. Per-label post-interpolative transfer functions together
with inter-label interpolation are performed in the pixel shader stage
in a single rendering pass, hence obtaining high-quality rendering of
labeled data on GPUs. The presented technique showed its high
practical value for the 3D-visualization of tiny vessel and nerve
structures in MR data in case of neurovascular compression syndromes.
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.
Caused by a contact between vascular structures and the root entry or exit zone of cranial nerves neurovascular compression syndromes are combined with different neurological diseases (trigeminal neurolagia, hemifacial spasm, vertigo, glossopharyngeal neuralgia) and show a relation with essential arterial hypertension. As presented previously, the semi-automatic segmentation and 3D visualization of strongly T2 weighted MR volumes has proven to be an effective strategy for a better spatial understanding prior to operative microvascular decompression. After explicit segmentation of coarse structures, the tiny target nerves and vessels contained in the area of cerebrospinal fluid are segmented implicitly using direct volume rendering. However, based on this strategy the delineation of vessels in the vicinity of the brainstem and those at the border of the segmented CSF subvolume are critical. Therefore, we suggest registration with MR angiography and introduce consecutive fusion after semi-automatic labeling of the vascular information. Additionally, we present an approach of automatic 3D visualization and video generation based on predefined flight paths. Thereby, a standardized evaluation of the fused image data is supported and the visualization results are optimally prepared for intraoperative application. Overall, our new strategy contributes to a significantly improved 3D representation and evaluation of vascular compression syndromes. Its value for diagnosis and surgery is demonstrated with various clinical examples.
Direct volume visualization of computer tomography data is based on the mapping of data values to colors and opacities with lookup-tables known as transfer functions (TF). Often, limitations of one-dimensional TF become evident when it comes to the visualization of aneurysms close the skull base. Computer tomography angiography data is used for the 3D-representation of the vessels filled with contrast medium. The reduced intensity differences between osseous tissue and contrast medium lead to strong artifacts and ambiguous visualizations. We introduced the use of bidimensional TFs based on measured intensities and gradient magnitudes for the visualization of aneurysms involving the skull base. The obtained results are clearly superior to a standard approach with one-dimensional TFs. Nevertheless, the additional degree of freedom increases the difficulty involved in creating adequate TFs. In order to address this problem, we introduce automatic adjustment of bidimensional TFs through a registration of respective 2D histograms. Initially, a dataset is set as reference and the information contained in its 2D histogram (intensities and gradient magnitudes) is used to create a TF template which produces a clear visualization of the vessels. When a new dataset is examined, elastic registration of the reference and target 2D histograms is carried out. The resulting free form deformation is then used for the automatic adjustment of the reference TF, in order to automatically obtain a clear volume visualization of the vascular structures within the examined dataset. Results are comparable to manually created TFs. This approach makes it possible to successfully use bidimensional TFs without technical insight and training.