To effectively use a virtual environment (VE) for applications such as training and design evaluation, a good sense of orientation is needed in the VE. “Natural” human geographical orientation, when moving around in the world, relies on visual as well as proprioceptive feedback. However, the present navigation metaphors that are used to move around in the VE often lack proprioceptive feedback. To investigate the possible consequences this may have, an experiment was conducted on the relative contributions of visual and proprioceptive feedback on path integration in VE. Subjects were immersed in a virtual forest and were asked to turn specific angles under different combinations of visual, vestibular, and kinesthetic feedback (pure visual, visual plus vestibular, visual plus vestibular plus kinesthetic, pure vestibular, and vestibular plus kinesthetic). Furthermore, two visual conditions with different visual flows were tested: normal visual flow and decreased visual flow provided by a 60% zoom. Results show that kinesthetic feedback provides the most reliable and accurate source of information to use for path integration, indicating the benefits of incorporating this kind of feedback in navigation metaphors. Orientation based on visual flow alone is most inaccurate and unreliable. In all conditions, subjects overestimated their turning speed and subsequently didn't turn far enough. Both the absolute errors and the variation in path integration increase with the length of the path.

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Recently, new techniques for minimally invasive aortic valve implantation have been developed generating a need for planning tools that assess valve anatomy and guidance tools that support implantation under x-ray guidance. Extracting the aortic valve anatomy from CT images is essential for such tools and we present a model-based method for that purpose. In addition, we present a new method for the detection of the coronary ostia that exploits the model-based segmentation and show, how a number of clinical measurements such as diameters and the distances between aortic valve plane and coronary ostia can be derived that are important for procedure planning. Validation results are based on accurate reference annotations of 20 CT images from different patients and leave-one-out tests. They show that model adaptation can be done with a mean surface-to-surface error of 0.5mm. For coronary ostia detection a success rate of 97.5% is achieved. Depending on the measured quantity, the segmentation translates into a root-mean-square error between 0.4 − 1.2mm when comparing clinical measurements derived from automatic segmentation and from reference annotations.

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