Feature emphasis and contextual cutaways for multimodal medical visualization

  • Authors:

  • Michael Burns;Martin Haidacher;Wolfgang Wein;Ivan Viola;M. Eduard Gröller

  • Affiliations:

  • Imaging & Visualization Department, Siemens Corporate Research, Princeton, NJ and Computer Science Department, Princeton University;Imaging & Visualization Department, Siemens Corporate Research, Princeton, NJ and Institute of Computer Graphics and Algorithms, Vienna University of Technology, Austria;Imaging & Visualization Department, Siemens Corporate Research, Princeton, NJ;Department of Informatics, University of Bergen, Norway;Institute of Computer Graphics and Algorithms, Vienna University of Technology, Austria and Department of Informatics, University of Bergen, Norway

  • Venue:
  • EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
  • Year:
  • 2007

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Abstract

Dense clinical data like 3D Computed Tomography (CT) scans can be visualized together with real-time imaging for a number of medical intervention applications. However, it is difficult to provide a fused visualization that allows sufficient spatial perception of the anatomy of interest, as derived from the rich pre-operative scan, while not occluding the real-time image displayed embedded within the volume. We propose an importance-driven approach that presents the embedded data such that it is clearly visible along with its spatial relation to the surrounding volumetric material. To support this, we present and integrate novel techniques for importance specification, feature emphasis, and contextual cutaway generation. We show results in a clinical context where a pre-operative CT scan is visualized alongside a tracked ultrasound image, such that the important vasculature is depicted between the viewpoint and the ultrasound image, while a more opaque representation of the anatomy is exposed in the surrounding area.