
pmid: 39259481
Optical-see-through head-mounted displays have the ability to seamlessly integrate virtual content with the real world through a transparent lens and an optical combiner. Although their potential for use in surgical settings has been explored, their clinical translation is sparse in the current literature, largely due to their limited tracking capabilities and the need for manual alignment of virtual representations of objects with their real-world counterparts.We propose a simple and robust hand-eye calibration process for the depth camera of the Microsoft HoloLens 2, utilizing a tracked surgical stylus fitted with infrared reflective spheres as the calibration tool.Using a Monte Carlo simulation and a paired-fiducial registration algorithm, we show that a calibration accuracy of 1.65 mm can be achieved with as little as 6 fiducial points. We also present heuristics for optimizing the accuracy of the calibration. The ability to use our calibration method in a clinical setting is validated through a user study, with users achieving a mean calibration accuracy of 1.67 mm in an average time of 42 s.This work enables real-time hand-eye calibration for the Microsoft HoloLens 2, without any need for a manual alignment process. Using this framework, existing surgical navigation systems employing optical or electromagnetic tracking can easily be incorporated into an augmented reality environment with a high degree of accuracy.
Surgery, Computer-Assisted, Calibration, Humans, Monte Carlo Method, Algorithms
Surgery, Computer-Assisted, Calibration, Humans, Monte Carlo Method, Algorithms
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
