Evaluation of Different Visualization Techniques for Perception-Based Alignment in Medical AR

Many Augmented Reality (AR) applications require the alignment of virtual objects to the real world; this is particularly important in medical AR scenarios where medical imaging information may be displayed directly on a patient and is used to identify the exact locations of specific anatomical structures within the body. For optical see-through AR, alignment accuracy depends both on the optical parameters of the AR display as well as the visualization parameters of the virtual model. In this paper, we explore how different static visualization techniques influence users' ability to perform perception-based alignment in AR for breast reconstruction surgery, where surgeons must accurately identify the locations of several perforator blood vessels while planning the procedure. We conducted a pilot study in which four subjects used four different visualization techniques with varying degrees of opaqueness and brightness as well as outline contrast to align virtual replicas of the relevant anatomy to their 3D-printed counterparts. We collected quantitative scores on spatial alignment accuracy using an external tracking system and qualitative scores on user preference and perceived performance. Results indicate that the highest source of alignment error was along the depth dimension, with users consistently overestimating depth when aligning the virtual renderings. The majority of subjects preferred visualization techniques rendered with lower levels of opaqueness and brightness as well as higher outline contrast, which were also found to support more accurate alignment.