Abstract

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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