Improving Spatial Perception for Medical Augmented Reality with Interactable Depth Layers

不正确的空间解释仍然是医学增强现实（AR）中最常见的感知问题之一。为了进一步研究这一挑战，我们的项目将详细阐述可以改善医疗AR空间感知的新方法。现有方法通常不足以在投影或光学透视AR中探索医疗3D数据。虽然旨在为整个数据集提供额外的深度信息，但许多现有方法使场景变得过于混乱，从而束缚了宝贵的精神资源并可能放大无意盲视，因此，我们将开发和评估新的多层AR可视化和交互技术。我们的目标是确定深度层分解是否有助于更好地理解医学3D数据的空间关系，以及透明度是否可以促进多层可视化的深度感知。此外，我们将研究多模式和协作交互的方法是否有助于减少当前显示的AR信息量。该项目的结果将为医学AR中多层信息的表示提供新的见解。这些见解可用于增强已建立的AR可视化技术，提高其可用性，从而降低AR引导的医疗干预期间的风险。

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 an 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.