Multicenter Assessment of Augmented Reality Registration Methods for Image-guided Interventions
Ningcheng Li, Jonathan Wakim, Yilun Koethe, Timothy Huber, Terence, Gade, Stephen Hunt, Brian Park

TL;DR
This study compares manual and automatic augmented reality registration methods using HoloLens 2 for image-guided interventions, showing improved manual registration times with hand gestures and comparable accuracy to automatic methods.
Contribution
It provides a comprehensive assessment of registration times and accuracy across different methods and experience levels, highlighting improvements over HoloLens 1 and potential for clinical use.
Findings
Automatic registration was significantly faster than manual methods.
Hand gesture registration times improved with practice.
Manual registration accuracy was comparable across methods.
Abstract
Purpose: To evaluate manual and automatic registration times as well as accuracy with augmented reality during alignment of a holographic 3-dimensional (3D) model onto the real-world environment. Method: 18 participants in various stages of clinical training across two academic centers registered a 3D CT phantom model onto a CT grid using the HoloLens 2 augmented reality headset 3 consecutive times. Registration times and accuracy were compared among different registration methods (hand gesture, Xbox controller, and automatic registration), levels of clinical experience, and consecutive attempts. Registration times were also compared with prior HoloLens 1 data. Results: Mean aggregate manual registration times were 27.7, 24.3, and 72.8 seconds for one-handed gesture, two-handed gesture, and Xbox controller, respectively; mean automatic registration time was 5.3s (ANOVA p<0.0001). No…
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Taxonomy
TopicsAugmented Reality Applications · Anatomy and Medical Technology · Surgical Simulation and Training
