VMLS: Visualization in Medicine and Life Sciences

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https://diglib.eg.org/handle/10.2312/1011

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Browsing VMLS: Visualization in Medicine and Life Sciences by Subject "Life and Medical Sciences"

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    Interactive Visualization of Neuroanatomical Data for a Hands-On Multimedia Exhibit
    (The Eurographics Association, 2013) Rieder, C.; Brachmann, C.; Hofmann, B.; Klein, J.; Köhn, A.; Ojdanic, D.; Schumann, C.; Weiler, F.; Hahn, H. K.; L. Linsen and H. -C. Hege and B. Hamann
    Magnetic resonance imaging is a technique which is routinely used by neuroradiologists. Within the last decade, several techniques have been developed to visualize those MR images so that medical experts, and thus the patients, can benefit from it. However, very little work has been done to use neuroanatomical MR data for educational purposes and to bring the general public into closer contact with the scientific knowledge. In this paper, an interactive visualization of neuroanatomical data, which is controlled by a dedicated user input device, is presented for a novel neuroscience exhibit. State-of-the-art visualization methods are combined to facilitate easy perception of the complexity of the medical data. For that, fiber tubes and diffusion-weighted image overlays are integrated into a volume rendering of the brain. Ambient occlusion algorithms are utilized to calculate self-shadowing of the brain anatomy and the fiber tubes. Further, a physical model of the brain and a touch display are used as user input devices. The visibility of fiber bundles can be intuitively controlled by activating touch sensors, which have been inserted into the physical brain model at the corresponding functional areas.
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    Visualization for Understanding Uncertainty in the Simulation of Myocardial Ischemia
    (The Eurographics Association, 2013) Rosen, Paul; Burton, Brett; Potter, Kristin; Johnson, Chris R.; L. Linsen and H. -C. Hege and B. Hamann
    We have created the Myocardial Uncertainty Viewer (muView or µView) tool for exploring data stemming from the forward simulation of cardiac ischemia. The simulation uses a collection of conductivity values to understand how ischemic regions effect the undamaged anisotropic heart tissue. The data resulting from the simulation is multivalued and volumetric and thus, for every data point, we have a collection of samples describing cardiac electrical properties. µView combines a suite of visual analysis methods to explore the area surrounding the ischemic zone and identify how perturbations of variables changes the propagation of their effects.