Improving Spatial Perception for Medical Augmented Reality with Interactable Depth Layers

Incorrect spatial interpretation is still one of the most common perceptional problems in medical augmented reality (AR). To further investigate this challenge, our project will elaborate on new methods that can improve the spatial perception for medical AR. Existing approaches are often not sufficient to explore medical 3D data in projected or optical see-through AR. While aiming at providing additional depth information for the whole dataset, many current approaches clutter the scene with too much information, thus binding valuable mental resources and potentially amplifying inattentional blindness.

Therefore, we will develop and evaluate new visualization ans interaction techniques for multilayer AR. Our objective is to determine if depth layer decompositions help to better understand spatial relations of medical 3D data, and if transparency can facilitate depth perception for multi-layer visualizations. In addition, we will investigate whether methods for multimodal and collaborative interaction can help to reduce the amount of currently displayed AR information. The results of this project should gain new insights for the representation of multilayer information in medical AR. These insights could be used to enhance established AR visualization techniques, to increase its usability, and thus to reduce risks during AR-guided medical interventions.