A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system

Abstract

Background: Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty.

Methods: A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration.

Results: Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18–26 mm) and different support radii (2–6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm.

Conclusion: This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

Keywords:

• Neuronavigation
• surface registration
• 3D feature matching

Acknowledgements

This study was supported by National Natural Science Foundation of China (project 81471758) and Shanghai Natural Science Foundation (project 17ZR1401500). This study was partially funded under the Program of Shanghai Academic/Technology Research Leader project 16XD1424900 and the National Science and Technology Support Program (No.2015BAK31B01).

Disclosure statement

No potential conflict of interest was reported by the author(s).