Depth-based registration of 3D preoperative models to intraoperative patient anatomy using the HoloLens 2.

Abstract

Purpose: In augmented reality (AR) surgical navigation, a registration step is required to align the preoperative data with the patient. This work investigates the use of the depth sensor of HoloLens 2 for registration in surgical navigation.

Methods: An AR depth-based registration framework was developed. The framework aligns preoperative and intraoperative point clouds and overlays the preoperative model on the patient. For evaluation, three experiments were conducted. First, the accuracy of the HoloLens's depth sensor was evaluated for both Long-Throw (LT) and Articulated Hand Tracking (AHAT) modes. Second, the overall registration accuracy was assessed with different alignment approaches. The accuracy and success rate of each approach were evaluated. Finally, a qualitative assessment of the framework was performed on various objects.

Results: The depth accuracy experiment showed mean overestimation errors of 5.7 mm for AHAT and 9.0 mm for LT. For the overall alignment, the mean translation errors of the different methods ranged from 12.5 to 17.0 mm, while rotation errors ranged from 0.9 to 1.1 degrees.

Conclusion: The results show that the depth sensor on the HoloLens 2 can be used for image-to-patient alignment with 1-2 cm accuracy and within 4 s, indicating that with further improvement in the accuracy, this approach can offer a convenient alternative to other time-consuming marker-based approaches. This work provides a generic marker-less registration framework using the depth sensor of the HoloLens 2, with extensive analysis of the sensor's reconstruction and registration accuracy. It supports advancing the research of marker-less registration in surgical navigation.