PURPOSE: Mixed reality (MR) for image-guided surgery may enable unobtrusive solutions for precision surgery. To display preoperative treatment plans at the correct physical position, it is essential to spatially align it with the patient intra-operatively. Accurate alignment is safety critical because it will guide treatment, but cannot always be achieved for varied reasons. Effective visualization mechanisms that reveal misalignment are crucial to prevent adverse surgical outcomes to ensure safe execution. METHODS: We test the effectiveness of three MR visualization paradigms in revealing spatial misalignment: wireframe, silhouette, and heatmap, which encodes residual registration error. We conduct a user study among 12 participants and use an anthropomorphic phantom mimicking total shoulder arthroplasty. Participants wearing Microsoft HoloLens 2 are presented with 36 randomly ordered spatial (mis)alignments of a virtual glenoid model overlaid on the phantom, each rendered using one of the three methods. Users choose whether to accept or reject the spatial alignment at every trial. Upon completion, participants report their perceived difficulty while using the visualization paradigms. RESULTS: Across all visualization paradigms, the ability of participants to reliably judge the accuracy of spatial alignment was moderate (58.33%).The three visualization paradigms showed comparable performance. However, the heatmap-based visualization resulted in significantly better detectability than random chance ([Formula: see text]). Despite heatmap enabling the most accurate decisions according to our measurements, wireframe was the most liked paradigm (50 %), followed by silhouette (41.7 %) and heatmap (8.3 %). CONCLUSION: Our findings suggest that conventional mixed reality visualization paradigms are not sufficiently effective in enabling users to differentiate between accurate and inaccurate spatial alignment of virtual content to the environment.
PURPOSE: Mixed reality (MR) for image-guided surgery may enable unobtrusive solutions for precision surgery. To display preoperative treatment plans at the correct physical position, it is essential to spatially align it with the patient intra-operatively. Accurate alignment is safety critical because it will guide treatment, but cannot always be achieved for varied reasons. Effective visualization mechanisms that reveal misalignment are crucial to prevent adverse surgical outcomes to ensure safe execution. METHODS: We test the effectiveness of three MR visualization paradigms in revealing spatial misalignment: wireframe, silhouette, and heatmap, which encodes residual registration error. We conduct a user study among 12 participants and use an anthropomorphic phantom mimicking total shoulder arthroplasty. Participants wearing Microsoft HoloLens 2 are presented with 36 randomly ordered spatial (mis)alignments of a virtual glenoid model overlaid on the phantom, each rendered using one of the three methods. Users choose whether to accept or reject the spatial alignment at every trial. Upon completion, participants report their perceived difficulty while using the visualization paradigms. RESULTS: Across all visualization paradigms, the ability of participants to reliably judge the accuracy of spatial alignment was moderate (58.33%).The three visualization paradigms showed comparable performance. However, the heatmap-based visualization resulted in significantly better detectability than random chance ([Formula: see text]). Despite heatmap enabling the most accurate decisions according to our measurements, wireframe was the most liked paradigm (50 %), followed by silhouette (41.7 %) and heatmap (8.3 %). CONCLUSION: Our findings suggest that conventional mixed reality visualization paradigms are not sufficiently effective in enabling users to differentiate between accurate and inaccurate spatial alignment of virtual content to the environment.
Authors: J Edward Swan; Adam Jones; Eric Kolstad; Mark A Livingston; Harvey S Smallman Journal: IEEE Trans Vis Comput Graph Date: 2007 May-Jun Impact factor: 4.579