Prof. Daniel Weiskopf Profile

Prof. Daniel Weiskopf

at Simon Fraser Univ

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Proceedings Article | 22 March 2007 Paper

Yonas Weldeselassie, Ghassan Hamarneh, Daniel Weiskopf

Proceedings Volume 6509, 65092M (2007) https://doi.org/10.1117/12.710417

KEYWORDS: Visualization, Diffusion, Distance measurement, Image visualization, Anisotropy, Brain, Tissues, Medical imaging, Visual analytics, Magnetic resonance imaging

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In this paper, we propose an adaptive seeding strategy for visualization of diffusion tensor magnetic resonance imaging (DT-MRI) data using streamtubes. DT-MRI is a medical imaging modality that captures unique water diffusion properties and fiber orientation information of the imaged tissues. Visualizing DT-MRI data using streamtubes has the advantage that not only the anisotropic nature of the diffusion is visualized but also the underlying anatomy of biological structures is revealed. This makes streamtubes significant for the analysis of fibrous tissues in medical images. In order to avoid rendering multiple similar streamtubes, an adaptive seeding strategy is employed which takes into account similarity of tensors in a given region. The goal is to automate the process of generating seed points such that regions with dissimilar tensors are assigned more seed points compared to regions with similar tensors. The algorithm is based on tensor dissimilarity metrics that take into account both diffusion magnitudes and directions to optimize the seeding positions and density of streamtubes in order to reduce the visual clutter. Two recent advances in tensor calculus and tensor dissimilarity metrics are utilized: the Log-Euclidean and the J-divergence. Results show that adaptive seeding not only helps to cull unnecessary streamtubes that would obscure visualization but also do so without having to compute the culled streamtubes, which makes the visualization process faster.

Proceedings Article | 20 March 2007 Paper

Simultaneous visualization of anatomical and functional 3D data by combining volume rendering and flow visualization

Tobias Schafhitzel, Friedemann Rößler, Daniel Weiskopf, Thomas Ertl

Proceedings Volume 6509, 650902 (2007) https://doi.org/10.1117/12.708799

KEYWORDS: Visualization, Brain activation, Brain, Functional magnetic resonance imaging, Volume rendering, Digital video recorders, Opacity, Particles, Convolution, 3D metrology

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Modern medical imaging provides a variety of techniques for the acquisition of multi-modality data. A typical example is the combination of functional and anatomical data from functional Magnetic Resonance Imaging (fMRI) and anatomical MRI measurements. Usually, the data resulting from each of these two methods is transformed to 3D scalar-field representations to facilitate visualization. A common method for the visualization of anatomical/functional multi-modalities combines semi-transparent isosurfaces (SSD, surface shaded display) with other scalar visualization techniques like direct volume rendering (DVR). However, partial occlusion and visual clutter that typically result from the overlay of these traditional 3D scalar-field visualization techniques make it difficult for the user to perceive and recognize visual structures. This paper addresses these perceptual issues by a new visualization approach for anatomical/functional multi-modalities. The idea is to reduce the occlusion effects of an isosurface by replacing its surface representation by a sparser line representation. Those lines are chosen along the principal curvature directions of the isosurface and rendered by a flow visualization method called line integral convolution (LIC). Applying the LIC algorithm results in fine line structures that improve the perception of the isosurface's shape in a way that it is possible to render it with small opacity values. An interactive visualization is achieved by executing the algorithm completely on the graphics processing unit (GPU) of modern graphics hardware. Furthermore, several illumination techniques and image compositing strategies are discussed for emphasizing the isosurface structure. We demonstrate our method for the example of fMRI/MRI measurements, visualizing the spatial relationship between brain activation and brain tissue.

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